[Frontend] Add `logits_processors` as an extra completion argument (#11150)

Signed-off-by: Brad Hilton <brad.hilton.nw@gmail.com>

tests/entrypoints/openai/test_serving_chat.py

@@ -30,6 +30,7 @@ class MockModelConfig:
     tokenizer_revision = None
     multimodal_config = MultiModalConfig()
     hf_config = MockHFConfig()
+    logits_processor_pattern = None
 
 
 @dataclass

vllm/config.py

@@ -156,41 +156,45 @@ class ModelConfig:
             can not be gathered from the vllm arguments.
         override_pooler_config: Initialize non default pooling config or
             override default pooling config for the pooling model.
+        logits_processor_pattern: 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.
     """
 
-    def __init__(
-            self,
-            model: str,
-            task: Union[TaskOption, Literal["draft"]],
-            tokenizer: str,
-            tokenizer_mode: str,
-            trust_remote_code: bool,
-            dtype: Union[str, torch.dtype],
-            seed: int,
-            allowed_local_media_path: str = "",
-            revision: Optional[str] = None,
-            code_revision: Optional[str] = None,
-            rope_scaling: Optional[Dict[str, Any]] = None,
-            rope_theta: Optional[float] = None,
-            tokenizer_revision: Optional[str] = None,
-            max_model_len: Optional[int] = None,
-            spec_target_max_model_len: Optional[int] = None,
-            quantization: Optional[str] = None,
-            quantization_param_path: Optional[str] = None,
-            enforce_eager: Optional[bool] = None,
-            max_seq_len_to_capture: Optional[int] = None,
-            max_logprobs: int = 20,
-            disable_sliding_window: bool = False,
-            skip_tokenizer_init: bool = False,
-            served_model_name: Optional[Union[str, List[str]]] = None,
-            limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
-            use_async_output_proc: bool = True,
-            config_format: ConfigFormat = ConfigFormat.AUTO,
-            hf_overrides: Optional[HfOverrides] = None,
-            mm_processor_kwargs: Optional[Dict[str, Any]] = None,
-            mm_cache_preprocessor: bool = False,
-            override_neuron_config: Optional[Dict[str, Any]] = None,
-            override_pooler_config: Optional["PoolerConfig"] = None) -> None:
+    def __init__(self,
+                 model: str,
+                 task: Union[TaskOption, Literal["draft"]],
+                 tokenizer: str,
+                 tokenizer_mode: str,
+                 trust_remote_code: bool,
+                 dtype: Union[str, torch.dtype],
+                 seed: int,
+                 allowed_local_media_path: str = "",
+                 revision: Optional[str] = None,
+                 code_revision: Optional[str] = None,
+                 rope_scaling: Optional[Dict[str, Any]] = None,
+                 rope_theta: Optional[float] = None,
+                 tokenizer_revision: Optional[str] = None,
+                 max_model_len: Optional[int] = None,
+                 spec_target_max_model_len: Optional[int] = None,
+                 quantization: Optional[str] = None,
+                 quantization_param_path: Optional[str] = None,
+                 enforce_eager: Optional[bool] = None,
+                 max_seq_len_to_capture: Optional[int] = None,
+                 max_logprobs: int = 20,
+                 disable_sliding_window: bool = False,
+                 skip_tokenizer_init: bool = False,
+                 served_model_name: Optional[Union[str, List[str]]] = None,
+                 limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
+                 use_async_output_proc: bool = True,
+                 config_format: ConfigFormat = ConfigFormat.AUTO,
+                 hf_overrides: Optional[HfOverrides] = None,
+                 mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+                 mm_cache_preprocessor: bool = False,
+                 override_neuron_config: Optional[Dict[str, Any]] = None,
+                 override_pooler_config: Optional["PoolerConfig"] = None,
+                 logits_processor_pattern: Optional[str] = None) -> None:
         self.model = model
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
@@ -316,6 +320,7 @@ class ModelConfig:
         self.task: Final = task
 
         self.pooler_config = self._init_pooler_config(override_pooler_config)
+        self.logits_processor_pattern = logits_processor_pattern
 
         self._verify_quantization()
         self._verify_cuda_graph()

vllm/engine/arg_utils.py

@@ -170,6 +170,7 @@ class EngineArgs:
     enable_chunked_prefill: Optional[bool] = None
 
     guided_decoding_backend: str = 'xgrammar'
+    logits_processor_pattern: Optional[str] = None
     # Speculative decoding configuration.
     speculative_model: Optional[str] = None
     speculative_model_quantization: Optional[str] = None
@@ -374,6 +375,14 @@ class EngineArgs:
             'https://github.com/noamgat/lm-format-enforcer.'
             ' Can be overridden per request via guided_decoding_backend'
             ' parameter.')
+        parser.add_argument(
+            '--logits-processor-pattern',
+            type=nullable_str,
+            default=None,
+            help='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.')
         # Parallel arguments
         parser.add_argument(
             '--distributed-executor-backend',
@@ -975,7 +984,7 @@ class EngineArgs:
             mm_cache_preprocessor=self.mm_cache_preprocessor,
             override_neuron_config=self.override_neuron_config,
             override_pooler_config=self.override_pooler_config,
-        )
+            logits_processor_pattern=self.logits_processor_pattern)
 
     def create_load_config(self) -> LoadConfig:
         return LoadConfig(

vllm/entrypoints/openai/protocol.py

 # Adapted from
 # https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
+import re
 import time
 from argparse import Namespace
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -14,7 +15,7 @@ from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
                                   RequestOutputKind, SamplingParams)
 from vllm.sequence import Logprob
-from vllm.utils import random_uuid
+from vllm.utils import random_uuid, resolve_obj_by_qualname
 
 logger = init_logger(__name__)
 
@@ -148,6 +149,46 @@ class ChatCompletionNamedToolChoiceParam(OpenAIBaseModel):
     type: Literal["function"] = "function"
 
 
+class LogitsProcessorConstructor(BaseModel):
+    qualname: str
+    args: Optional[List[Any]] = None
+    kwargs: Optional[Dict[str, Any]] = None
+
+
+LogitsProcessors = List[Union[str, LogitsProcessorConstructor]]
+
+
+def get_logits_processors(processors: Optional[LogitsProcessors],
+                          pattern: Optional[str]) -> Optional[List[Any]]:
+    if processors and pattern:
+        logits_processors = []
+        for processor in processors:
+            qualname = processor if isinstance(processor,
+                                               str) else processor.qualname
+            if not re.match(pattern, qualname):
+                raise ValueError(
+                    f"Logits processor '{qualname}' is not allowed by this "
+                    "server. See --logits-processor-pattern engine argument "
+                    "for more information.")
+            try:
+                logits_processor = resolve_obj_by_qualname(qualname)
+            except Exception as e:
+                raise ValueError(
+                    f"Logits processor '{qualname}' could not be resolved: {e}"
+                ) from e
+            if isinstance(processor, LogitsProcessorConstructor):
+                logits_processor = logits_processor(*processor.args or [],
+                                                    **processor.kwargs or {})
+            logits_processors.append(logits_processor)
+        return logits_processors
+    elif processors:
+        raise ValueError(
+            "The `logits_processors` argument is not supported by this "
+            "server. See --logits-processor-pattern engine argugment "
+            "for more information.")
+    return None
+
+
 class ChatCompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/chat/create
@@ -293,6 +334,17 @@ class ChatCompletionRequest(OpenAIBaseModel):
             "The request_id related to this request. If the caller does "
             "not set it, a random_uuid will be generated. This id is used "
             "through out the inference process and return in response."))
+    logits_processors: Optional[LogitsProcessors] = 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'}}."))
 
     # doc: end-chat-completion-extra-params
 
@@ -314,7 +366,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
             length_penalty=self.length_penalty,
             include_stop_str_in_output=self.include_stop_str_in_output)
 
-    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+    def to_sampling_params(
+            self, default_max_tokens: int,
+            logits_processor_pattern: Optional[str]) -> SamplingParams:
         # TODO(#9845): remove max_tokens when field is removed from OpenAI API
         max_tokens = self.max_completion_tokens or self.max_tokens
         if max_tokens is None:
@@ -364,6 +418,8 @@ 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 if self.stream \
@@ -599,6 +655,17 @@ class CompletionRequest(OpenAIBaseModel):
             "The priority of the request (lower means earlier handling; "
             "default: 0). Any priority other than 0 will raise an error "
             "if the served model does not use priority scheduling."))
+    logits_processors: Optional[LogitsProcessors] = 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'}}."))
 
     # doc: end-completion-extra-params
 
@@ -619,7 +686,9 @@ class CompletionRequest(OpenAIBaseModel):
             length_penalty=self.length_penalty,
             include_stop_str_in_output=self.include_stop_str_in_output)
 
-    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+    def to_sampling_params(
+            self, default_max_tokens: int,
+            logits_processor_pattern: Optional[str]) -> SamplingParams:
         max_tokens = self.max_tokens
         if max_tokens is None:
             max_tokens = default_max_tokens
@@ -665,6 +734,8 @@ 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 \
                 else RequestOutputKind.FINAL_ONLY,

vllm/entrypoints/openai/serving_chat.py

@@ -197,7 +197,8 @@ class OpenAIServingChat(OpenAIServing):
                         default_max_tokens)
                 else:
                     sampling_params = request.to_sampling_params(
-                        default_max_tokens)
+                        default_max_tokens,
+                        self.model_config.logits_processor_pattern)
 
                 self._log_inputs(request_id,
                                  request_prompts[i],

vllm/entrypoints/openai/serving_completion.py

@@ -123,7 +123,8 @@ class OpenAIServingCompletion(OpenAIServing):
                         default_max_tokens)
                 else:
                     sampling_params = request.to_sampling_params(
-                        default_max_tokens)
+                        default_max_tokens,
+                        self.model_config.logits_processor_pattern)
 
                 request_id_item = f"{request_id}-{i}"