Improve-mm-and-pooler-and-decoding-configs (#16789)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/source/models/supported_models.md

@@ -788,7 +788,7 @@ llm = LLM(
 Online serving:
 
 ```bash
-vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt image=4
+vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt '{"image":4}'
 ```
 
 **This is no longer required if you are using vLLM V1.**

docs/source/serving/multimodal_inputs.md

@@ -228,7 +228,7 @@ First, launch the OpenAI-compatible server:
 
 ```bash
 vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
-  --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2
+  --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt '{"image":2}'
 ```
 
 Then, you can use the OpenAI client as follows:

examples/offline_inference/mistral-small.py

@@ -16,11 +16,11 @@ from vllm.sampling_params import SamplingParams
 # # Mistral format
 # vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 \
 #   --tokenizer-mode mistral --config-format mistral --load-format mistral \
-#   --limit-mm-per-prompt 'image=4' --max-model-len 16384
+#   --limit-mm-per-prompt '{"image":4}' --max-model-len 16384
 #
 # # HF format
 # vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 \
-#   --limit-mm-per-prompt 'image=4' --max-model-len 16384
+#   --limit-mm-per-prompt '{"image":4}' --max-model-len 16384
 # ```
 #
 # - Client:

examples/online_serving/openai_chat_completion_client_for_multimodal.py

@@ -9,7 +9,7 @@ vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
 
 (multi-image inference with Phi-3.5-vision-instruct)
 vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
-    --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2
+    --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt '{"image":2}'
 
 (audio inference with Ultravox)
 vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b --max-model-len 4096

tests/engine/test_arg_utils.py

@@ -24,6 +24,10 @@ from vllm.utils import FlexibleArgumentParser
     }),
 ])
 def test_limit_mm_per_prompt_parser(arg, expected):
+    """This functionality is deprecated and will be removed in the future.
+    This argument should be passed as JSON string instead.
+    
+    TODO: Remove with nullable_kvs."""
     parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
     if arg is None:
         args = parser.parse_args([])

tests/entrypoints/openai/test_audio.py

@@ -27,7 +27,7 @@ def server():
         "--enforce-eager",
         "--trust-remote-code",
         "--limit-mm-per-prompt",
-        f"audio={MAXIMUM_AUDIOS}",
+        str({"audio": MAXIMUM_AUDIOS}),
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:

tests/entrypoints/openai/test_video.py

@@ -31,7 +31,7 @@ def server():
         "--enforce-eager",
         "--trust-remote-code",
         "--limit-mm-per-prompt",
-        f"video={MAXIMUM_VIDEOS}",
+        str({"video": MAXIMUM_VIDEOS}),
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:

tests/entrypoints/openai/test_vision.py

@@ -35,7 +35,7 @@ def server():
         "--enforce-eager",
         "--trust-remote-code",
         "--limit-mm-per-prompt",
-        f"image={MAXIMUM_IMAGES}",
+        str({"image": MAXIMUM_IMAGES}),
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:

tests/entrypoints/openai/test_vision_embedding.py

@@ -37,7 +37,7 @@ def server():
         "--enforce-eager",
         "--trust-remote-code",
         "--limit-mm-per-prompt",
-        f"image={MAXIMUM_IMAGES}",
+        str({"image": MAXIMUM_IMAGES}),
         "--chat-template",
         str(vlm2vec_jinja_path),
     ]

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -48,9 +48,9 @@ def audio(request):
 ])
 def server(request, audio_assets):
     args = [
-        "--dtype=bfloat16", "--max-model-len=4096", "--enforce-eager",
-        f"--limit-mm-per-prompt=audio={len(audio_assets)}",
-        "--trust-remote-code"
+        "--dtype", "bfloat16", "--max-model-len", "4096", "--enforce-eager",
+        "--limit-mm-per-prompt",
+        str({"audio": len(audio_assets)}), "--trust-remote-code"
     ] + [
         f"--{key.replace('_','-')}={value}"
         for key, value in request.param.items()

vllm/config.py

@@ -17,7 +17,7 @@ from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
 from importlib.util import find_spec
 from pathlib import Path
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Final, Literal,
-                    Optional, Protocol, TypeVar, Union)
+                    Optional, Protocol, TypeVar, Union, get_args)
 
 import torch
 from pydantic import BaseModel, Field, PrivateAttr
@@ -2725,6 +2725,7 @@ class PromptAdapterConfig:
                                                 self.prompt_adapter_dtype)
 
 
+@config
 @dataclass
 class MultiModalConfig:
     """Controls the behavior of multimodal models."""
@@ -2732,6 +2733,8 @@ class MultiModalConfig:
     limit_per_prompt: Mapping[str, int] = field(default_factory=dict)
     """
     The maximum number of input items allowed per prompt for each modality.
+    This should be a JSON string that will be parsed into a dictionary.
+    Defaults to 1 (V0) or 999 (V1) for each modality.
     """
 
     def compute_hash(self) -> str:
@@ -2753,24 +2756,20 @@ class MultiModalConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    def get_default_limit_per_prompt(self) -> int:
-        """
-        Return the default number of input items allowed per prompt
-        for any modality if not specified by the user.
-        """
-        return 999 if envs.VLLM_USE_V1 else 1
-
     def get_limit_per_prompt(self, modality: str) -> int:
         """
         Get the maximum number of input items allowed per prompt
         for the given modality.
         """
-        default = self.get_default_limit_per_prompt()
-        return self.limit_per_prompt.get(modality, default)
+        return self.limit_per_prompt.get(
+            modality,
+            999 if envs.VLLM_USE_V1 else 1,
+        )
 
     # TODO: Add configs to init vision tower or not.
 
 
+@config
 @dataclass
 class PoolerConfig:
     """Controls the behavior of output pooling in pooling models."""
@@ -3095,15 +3094,28 @@ def get_served_model_name(model: str,
     return served_model_name
 
 
+GuidedDecodingBackendV0 = Literal["auto", "outlines", "lm-format-enforcer",
+                                  "xgrammar"]
+GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance"]
+
+
+@config
 @dataclass
 class DecodingConfig:
-    """Dataclass which contains the decoding strategy of the engine"""
+    """Dataclass which contains the decoding strategy of the engine."""
 
-    # Which guided decoding algo to use.
-    # 'outlines' / 'lm-format-enforcer' / 'xgrammar'
-    guided_decoding_backend: str = "auto" if envs.VLLM_USE_V1 else "xgrammar"
+    guided_decoding_backend: Union[
+        GuidedDecodingBackendV0,
+        GuidedDecodingBackendV1] = "auto" if envs.VLLM_USE_V1 else "xgrammar"
+    """Which engine will be used for guided decoding (JSON schema / regex etc)
+    by default. With "auto", we will make opinionated choices based on request
+    contents and what the backend libraries currently support, so the behavior
+    is subject to change in each release."""
 
     reasoning_backend: Optional[str] = None
+    """Select the reasoning parser depending on the model that you're using.
+    This is used to parse the reasoning content into OpenAI API format.
+    Required for `--enable-reasoning`."""
 
     def compute_hash(self) -> str:
         """
@@ -3125,17 +3137,12 @@ class DecodingConfig:
         return hash_str
 
     def __post_init__(self):
-        v0_valid_guided_backends = [
-            'outlines', 'lm-format-enforcer', 'xgrammar', 'auto'
-        ]
-        v1_valid_guided_backends = ['xgrammar', 'guidance', 'auto']
-
         backend = GuidedDecodingParams(
             backend=self.guided_decoding_backend).backend_name
         if envs.VLLM_USE_V1:
-            valid_guided_backends = v1_valid_guided_backends
+            valid_guided_backends = get_args(GuidedDecodingBackendV1)
         else:
-            valid_guided_backends = v0_valid_guided_backends
+            valid_guided_backends = get_args(GuidedDecodingBackendV0)
         if backend not in valid_guided_backends:
             raise ValueError(f"Invalid guided_decoding_backend '{backend}',"
                              f" must be one of {valid_guided_backends}")

vllm/engine/arg_utils.py

@@ -20,11 +20,12 @@ from vllm.config import (CacheConfig, CompilationConfig, Config, ConfigFormat,
                          DecodingConfig, Device, DeviceConfig,
                          DistributedExecutorBackend, HfOverrides,
                          KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
-                         ModelConfig, ModelImpl, ObservabilityConfig,
-                         ParallelConfig, PoolerConfig, PoolType,
-                         PromptAdapterConfig, SchedulerConfig, SchedulerPolicy,
-                         SpeculativeConfig, TaskOption, TokenizerPoolConfig,
-                         VllmConfig, get_attr_docs, get_field)
+                         ModelConfig, ModelImpl, MultiModalConfig,
+                         ObservabilityConfig, ParallelConfig, PoolerConfig,
+                         PoolType, PromptAdapterConfig, SchedulerConfig,
+                         SchedulerPolicy, SpeculativeConfig, TaskOption,
+                         TokenizerPoolConfig, VllmConfig, get_attr_docs,
+                         get_field)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
@@ -190,7 +191,8 @@ class EngineArgs:
         TokenizerPoolConfig.pool_type
     tokenizer_pool_extra_config: dict[str, Any] = \
         get_field(TokenizerPoolConfig, "extra_config")
-    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
+    limit_mm_per_prompt: Mapping[str, int] = \
+        get_field(MultiModalConfig, "limit_per_prompt")
     mm_processor_kwargs: Optional[Dict[str, Any]] = None
     disable_mm_preprocessor_cache: bool = False
     enable_lora: bool = False
@@ -252,7 +254,7 @@ class EngineArgs:
 
     additional_config: Optional[Dict[str, Any]] = None
     enable_reasoning: Optional[bool] = None
-    reasoning_parser: Optional[str] = None
+    reasoning_parser: Optional[str] = DecodingConfig.reasoning_backend
     use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
 
     def __post_init__(self):
@@ -478,18 +480,22 @@ class EngineArgs:
                             'Examples:\n'
                             '- 1k → 1000\n'
                             '- 1K → 1024\n')
-        parser.add_argument(
+
+        # Guided decoding arguments
+        guided_decoding_kwargs = get_kwargs(DecodingConfig)
+        guided_decoding_group = parser.add_argument_group(
+            title="DecodingConfig",
+            description=DecodingConfig.__doc__,
+        )
+        guided_decoding_group.add_argument(
             '--guided-decoding-backend',
-            type=str,
-            default=DecodingConfig.guided_decoding_backend,
-            help='Which engine will be used for guided decoding'
-            ' (JSON schema / regex etc) by default. Currently support '
-            'https://github.com/mlc-ai/xgrammar and '
-            'https://github.com/guidance-ai/llguidance.'
-            'Valid backend values are "xgrammar", "guidance", and "auto". '
-            'With "auto", we will make opinionated choices based on request '
-            'contents and what the backend libraries currently support, so '
-            'the behavior is subject to change in each release.')
+            **guided_decoding_kwargs["guided_decoding_backend"])
+        guided_decoding_group.add_argument(
+            "--reasoning-parser",
+            # This choices is a special case because it's not static
+            choices=list(ReasoningParserManager.reasoning_parsers),
+            **guided_decoding_kwargs["reasoning_backend"])
+
         parser.add_argument(
             '--logits-processor-pattern',
             type=optional_str,
@@ -697,18 +703,14 @@ class EngineArgs:
                                      **tokenizer_kwargs["extra_config"])
 
         # Multimodal related configs
-        parser.add_argument(
-            '--limit-mm-per-prompt',
-            type=nullable_kvs,
-            default=EngineArgs.limit_mm_per_prompt,
-            # The default value is given in
-            # MultiModalConfig.get_default_limit_per_prompt
-            help=('For each multimodal plugin, limit how many '
-                  'input instances to allow for each prompt. '
-                  'Expects a comma-separated list of items, '
-                  'e.g.: `image=16,video=2` allows a maximum of 16 '
-                  'images and 2 videos per prompt. Defaults to '
-                  '1 (V0) or 999 (V1) for each modality.'))
+        multimodal_kwargs = get_kwargs(MultiModalConfig)
+        multimodal_group = parser.add_argument_group(
+            title="MultiModalConfig",
+            description=MultiModalConfig.__doc__,
+        )
+        multimodal_group.add_argument('--limit-mm-per-prompt',
+                                      **multimodal_kwargs["limit_per_prompt"])
+
         parser.add_argument(
             '--mm-processor-kwargs',
             default=None,
@@ -1018,16 +1020,6 @@ class EngineArgs:
             "If enabled, the model will be able to generate reasoning content."
         )
 
-        parser.add_argument(
-            "--reasoning-parser",
-            type=str,
-            choices=list(ReasoningParserManager.reasoning_parsers),
-            default=None,
-            help=
-            "Select the reasoning parser depending on the model that you're "
-            "using. This is used to parse the reasoning content into OpenAI "
-            "API format. Required for ``--enable-reasoning``.")
-
         parser.add_argument(
             "--disable-cascade-attn",
             action="store_true",

vllm/multimodal/processing.py

 # SPDX-License-Identifier: Apache-2.0
+import json
 import re
 import sys
 from abc import ABC, abstractmethod
@@ -1117,8 +1118,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
 
             if num_items > allowed_limit:
                 raise ValueError(
-                    f"You set or defaulted to {modality}={allowed_limit} "
-                    f"in --limit-mm-per-prompt`, but passed {num_items} "
+                    "You set or defaulted to "
+                    f"'{json.dumps({modality: allowed_limit})}' in "
+                    f"`--limit-mm-per-prompt`, but passed {num_items} "
                     f"{modality} items in the same prompt.")
 
         return mm_items

vllm/multimodal/registry.py

 # SPDX-License-Identifier: Apache-2.0
 
 import functools
+import json
 from collections import UserDict
 from collections.abc import Mapping, Sequence
 from dataclasses import dataclass
@@ -194,9 +195,9 @@ class MultiModalRegistry:
             max_items = self._limits_by_model[model_config][data_key]
             if num_items > max_items:
                 raise ValueError(
-                    f"You set {data_key}={max_items} (or defaulted to 1) in "
-                    f"`--limit-mm-per-prompt`, but found {num_items} items "
-                    "in the same prompt.")
+                    f"You set '{json.dumps({data_key: max_items})}' (or "
+                    "defaulted to 1) in `--limit-mm-per-prompt`, but found "
+                    f"{num_items} items in the same prompt.")
 
             input_dict = plugin.map_input(model_config, data_value,
                                           mm_processor_kwargs)