[Feature] remove dependency on enable_mm and refine multimodal's code (#3014)

* remove dependency on enable_mm

* fix codestyle check error

* fix codestyle check error

* update docs

* resolve conflicts on model config

* fix unit test error

* fix code style check error

---------

Co-authored-by: shige <1021937542@qq.com>
Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>

docs/get_started/ernie-4.5-vl.md

@@ -31,7 +31,6 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --quantization wint4 \
        --max-model-len 32768 \
        --max-num-seqs 32 \
-       --enable-mm \
        --mm-processor-kwargs '{"video_max_frames": 30}' \
        --limit-mm-per-prompt '{"image": 10, "video": 3}' \
        --reasoning-parser ernie-45-vl

docs/get_started/quick_start_vl.md

@@ -26,8 +26,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --engine-worker-queue-port 8182 \
        --max-model-len 32768 \
        --max-num-seqs 32 \
-       --reasoning-parser ernie-45-vl \
-       --enable-mm
+       --reasoning-parser ernie-45-vl
 ```
 
 > 💡 Note: In the path specified by ```--model```, if the subdirectory corresponding to the path does not exist in the current directory, it will try to query whether AIStudio has a preset model based on the specified model name (such as ```baidu/ERNIE-4.5-0.3B-Base-Paddle```). If it exists, it will automatically start downloading. The default download path is: ```~/xx```. For instructions and configuration on automatic model download, see [Model Download](../supported_models.md).

docs/offline_inference.md

@@ -39,7 +39,7 @@ Documentation for `SamplingParams`, `LLM.generate`, `LLM.chat`, and output struc
 ```python
 from fastdeploy.entrypoints.llm import LLM
 # 加载模型
-llm = LLM(model="baidu/ERNIE-4.5-VL-28B-A3B-Paddle", tensor_parallel_size=1, max_model_len=32768, enable_mm=True, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
+llm = LLM(model="baidu/ERNIE-4.5-VL-28B-A3B-Paddle", tensor_parallel_size=1, max_model_len=32768, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
 
 outputs = llm.chat(
     messages=[
@@ -127,7 +127,7 @@ for message in messages:
             })
 
 sampling_params = SamplingParams(temperature=0.1, max_tokens=6400)
-llm = LLM(model=PATH, tensor_parallel_size=1, max_model_len=32768, enable_mm=True, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
+llm = LLM(model=PATH, tensor_parallel_size=1, max_model_len=32768, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
 outputs = llm.generate(prompts={
     "prompt": prompt,
     "multimodal_data": {

docs/parameters.md

@@ -19,7 +19,7 @@ When using FastDeploy to deploy models (including offline inference and service
 | ```tokenizer``` | `str` | Tokenizer name or path, defaults to model path |
 | ```use_warmup``` | `int` | Whether to perform warmup at startup, will automatically generate maximum length data for warmup, enabled by default when automatically calculating KV Cache |
 | ```limit_mm_per_prompt``` | `dict[str]` | Limit the amount of multimodal data per prompt, e.g.: {"image": 10, "video": 3}, default: 1 for all |
-| ```enable_mm``` | `bool` | Whether to support multimodal data (for multimodal models only), default: False |
+| ```enable_mm``` | `bool` | __[DEPRECATED]__ Whether to support multimodal data (for multimodal models only), default: False |
 | ```quantization``` | `str` | Model quantization strategy, when loading BF16 CKPT, specifying wint4 or wint8 supports lossless online 4bit/8bit quantization |
 | ```gpu_memory_utilization``` | `float` | GPU memory utilization, default: 0.9 |
 | ```num_gpu_blocks_override``` | `int` | Preallocated KVCache blocks, this parameter can be automatically calculated by FastDeploy based on memory situation, no need for user configuration, default: None |

docs/zh/get_started/ernie-4.5-vl.md

@@ -31,7 +31,6 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --quantization wint4 \
        --max-model-len 32768 \
        --max-num-seqs 32 \
-       --enable-mm \
        --mm-processor-kwargs '{"video_max_frames": 30}' \
        --limit-mm-per-prompt '{"image": 10, "video": 3}' \
        --reasoning-parser ernie-45-vl

docs/zh/get_started/quick_start_vl.md

@@ -26,8 +26,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --engine-worker-queue-port 8182 \
        --max-model-len 32768 \
        --max-num-seqs 32 \
-       --reasoning-parser ernie-45-vl \
-       --enable-mm
+       --reasoning-parser ernie-45-vl
 ```
 
 >💡 注意：在 ```--model``` 指定的路径中，若当前目录下不存在该路径对应的子目录，则会尝试根据指定的模型名称（如 ```baidu/ERNIE-4.5-0.3B-Base-Paddle```）查询AIStudio是否存在预置模型，若存在，则自动启动下载。默认的下载路径为：```~/xx```。关于模型自动下载的说明和配置参阅[模型下载](../supported_models.md)。

docs/zh/offline_inference.md

@@ -39,7 +39,7 @@ for output in outputs:
 ```python
 from fastdeploy.entrypoints.llm import LLM
 # 加载模型
-llm = LLM(model="baidu/ERNIE-4.5-VL-28B-A3B-Paddle", tensor_parallel_size=1, max_model_len=32768, enable_mm=True, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
+llm = LLM(model="baidu/ERNIE-4.5-VL-28B-A3B-Paddle", tensor_parallel_size=1, max_model_len=32768, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
 
 outputs = llm.chat(
     messages=[
@@ -127,7 +127,7 @@ for message in messages:
             })
 
 sampling_params = SamplingParams(temperature=0.1, max_tokens=6400)
-llm = LLM(model=PATH, tensor_parallel_size=1, max_model_len=32768, enable_mm=True, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
+llm = LLM(model=PATH, tensor_parallel_size=1, max_model_len=32768, limit_mm_per_prompt={"image": 100}, reasoning_parser="ernie-45-vl")
 outputs = llm.generate(prompts={
     "prompt": prompt,
     "multimodal_data": {

docs/zh/parameters.md

@@ -17,7 +17,7 @@
 | ```tokenizer```                    | `str`      | tokenizer 名或路径，默认为模型路径 |
 | ```use_warmup```                   | `int`      | 是否在启动时进行warmup，会自动生成极限长度数据进行warmup，默认自动计算KV Cache时会使用 |
 | ```limit_mm_per_prompt```          | `dict[str]` | 限制每个prompt中多模态数据的数量，如：{"image": 10, "video": 3}，默认都为1 |
-| ```enable_mm```                    | `bool`      | 是否支持多模态数据（仅针对多模模型），默认False |
+| ```enable_mm```                    | `bool`      | __[已废弃]__ 是否支持多模态数据（仅针对多模模型），默认False |
 | ```quantization```                 | `str`       | 模型量化策略，当在加载BF16 CKPT时，指定wint4或wint8时，支持无损在线4bit/8bit量化 |
 | ```gpu_memory_utilization```       | `float`     | GPU显存利用率，默认0.9 |
 | ```num_gpu_blocks_override```      | `int`       | 预分配KVCache块数，此参数可由FastDeploy自动根据显存情况计算，无需用户配置，默认为None |

fastdeploy/engine/args_utils.py

@@ -31,7 +31,7 @@ from fastdeploy.config import (
 )
 from fastdeploy.engine.config import Config
 from fastdeploy.scheduler.config import SchedulerConfig
-from fastdeploy.utils import FlexibleArgumentParser
+from fastdeploy.utils import DeprecatedOptionWarning, FlexibleArgumentParser
 
 
 def nullable_str(x: str) -> Optional[str]:
@@ -409,7 +409,7 @@ class EngineArgs:
         )
         model_group.add_argument(
             "--enable-mm",
-            action="store_true",
+            action=DeprecatedOptionWarning,
             default=EngineArgs.enable_mm,
             help="Flag to enable multi-modal model.",
         )
@@ -902,7 +902,7 @@ class EngineArgs:
             engine_worker_queue_port=self.engine_worker_queue_port,
             limit_mm_per_prompt=self.limit_mm_per_prompt,
             mm_processor_kwargs=self.mm_processor_kwargs,
-            enable_mm=self.enable_mm,
+            # enable_mm=self.enable_mm,
             reasoning_parser=self.reasoning_parser,
             splitwise_role=self.splitwise_role,
             innode_prefill_ports=self.innode_prefill_ports,

fastdeploy/engine/config.py

@@ -25,6 +25,7 @@ from fastdeploy.config import (
     ModelConfig,
     ParallelConfig,
 )
+from fastdeploy.multimodal.registry import MultimodalRegistry
 from fastdeploy.platforms import current_platform
 from fastdeploy.scheduler import SchedulerConfig
 from fastdeploy.utils import ceil_div, get_host_ip, is_port_available, llm_logger
@@ -78,7 +79,7 @@ class Config:
         engine_worker_queue_port: int = 8002,
         limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
         mm_processor_kwargs: Optional[Dict[str, Any]] = None,
-        enable_mm: bool = False,
+        # enable_mm: bool = False,
         splitwise_role: str = "mixed",
         innode_prefill_ports: Optional[List[int]] = None,
         max_num_partial_prefills: int = 1,
@@ -156,7 +157,7 @@ class Config:
         self.max_num_seqs = max_num_seqs
         self.limit_mm_per_prompt = limit_mm_per_prompt
         self.mm_processor_kwargs = mm_processor_kwargs
-        self.enable_mm = enable_mm
+        # self.enable_mm = enable_mm
         self.speculative_config = speculative_config
         self.use_warmup = use_warmup
         self.splitwise_role = splitwise_role
@@ -174,11 +175,19 @@ class Config:
 
         assert self.splitwise_role in ["mixed", "prefill", "decode"]
 
+        import fastdeploy.model_executor.models  # noqa: F401
+
+        architectures = self.model_config.architectures[0]
+        if MultimodalRegistry.contains_model(architectures):
+            self.enable_mm = True
+        else:
+            self.enable_mm = False
+
         # TODO
         self.max_prefill_batch = 3
         if current_platform.is_xpu():
             self.max_prefill_batch = 1
-        if enable_mm:
+        if self.enable_mm:
             self.max_prefill_batch = 1  # TODO:当前多模prefill阶段只支持并行度为1,待优化
 
         # TODO(@wufeisheng): TP and EP need to be supported simultaneously.

fastdeploy/entrypoints/chat_utils.py

@@ -27,8 +27,8 @@ from openai.types.chat import (
 )
 from typing_extensions import Required, TypeAlias, TypedDict
 
-from fastdeploy.input.multimodal.image import ImageMediaIO
-from fastdeploy.input.multimodal.video import VideoMediaIO
+from fastdeploy.multimodal.image import ImageMediaIO
+from fastdeploy.multimodal.video import VideoMediaIO
 
 
 class VideoURL(TypedDict, total=False):

fastdeploy/entrypoints/engine_client.py

@@ -19,9 +19,11 @@ import uuid
 
 import numpy as np
 
+from fastdeploy.engine.config import ModelConfig
 from fastdeploy.input.preprocess import InputPreprocessor
 from fastdeploy.inter_communicator import IPCSignal, ZmqClient
 from fastdeploy.metrics.work_metrics import work_process_metrics
+from fastdeploy.multimodal.registry import MultimodalRegistry
 from fastdeploy.platforms import current_platform
 from fastdeploy.utils import EngineError, api_server_logger
 
@@ -33,26 +35,34 @@ class EngineClient:
 
     def __init__(
         self,
+        model_name_or_path,
         tokenizer,
         max_model_len,
         tensor_parallel_size,
         pid,
         limit_mm_per_prompt,
         mm_processor_kwargs,
-        enable_mm=False,
+        # enable_mm=False,
         reasoning_parser=None,
         data_parallel_size=1,
         enable_logprob=False,
     ):
+        import fastdeploy.model_executor.models  # noqa: F401
+
+        architectures = ModelConfig({"model": model_name_or_path}).architectures[0]
+        if MultimodalRegistry.contains_model(architectures):
+            self.enable_mm = True
+        else:
+            self.enable_mm = False
+
         input_processor = InputPreprocessor(
             tokenizer,
             reasoning_parser,
             limit_mm_per_prompt,
             mm_processor_kwargs,
-            enable_mm,
+            self.enable_mm,
         )
         self.enable_logprob = enable_logprob
-        self.enable_mm = enable_mm
         self.reasoning_parser = reasoning_parser
         self.data_processor = input_processor.create_processor()
         self.max_model_len = max_model_len

fastdeploy/entrypoints/llm.py

@@ -28,9 +28,11 @@ from tqdm import tqdm
 from fastdeploy.engine.args_utils import EngineArgs
 from fastdeploy.engine.engine import LLMEngine
 from fastdeploy.engine.sampling_params import SamplingParams
-
-# from fastdeploy.entrypoints.chat_utils import ChatCompletionMessageParam
-from fastdeploy.utils import llm_logger, retrive_model_from_server
+from fastdeploy.utils import (
+    deprecated_kwargs_warning,
+    llm_logger,
+    retrive_model_from_server,
+)
 from fastdeploy.worker.output import Logprob, LogprobsLists
 
 root_logger = logging.getLogger()
@@ -72,6 +74,8 @@ class LLM:
         enable_logprob: Optional[bool] = False,
         **kwargs,
     ):
+        deprecated_kwargs_warning(**kwargs)
+
         model = retrive_model_from_server(model, revision)
         engine_args = EngineArgs(
             model=model,

fastdeploy/entrypoints/openai/api_server.py

@@ -105,13 +105,14 @@ async def lifespan(app: FastAPI):
         pid = os.getpid()
     api_server_logger.info(f"{pid}")
     engine_client = EngineClient(
+        args.model,
         args.tokenizer,
         args.max_model_len,
         args.tensor_parallel_size,
         pid,
         args.limit_mm_per_prompt,
         args.mm_processor_kwargs,
-        args.enable_mm,
+        # args.enable_mm,
         args.reasoning_parser,
         args.data_parallel_size,
         args.enable_logprob,

fastdeploy/input/preprocess.py

@@ -87,8 +87,8 @@ class InputPreprocessor:
                     reasoning_parser_obj=reasoning_parser_obj,
                 )
         else:
-            if not architectures.startswith("Ernie4_5_VLMoeForConditionalGeneration"):
-                raise ValueError(f"Model {self.model_name_or_path} is not a valid Ernie4_5_VLMoe model.")
+            if not ErnieArchitectures.contains_ernie_arch(architectures):
+                raise ValueError(f"Model {self.model_name_or_path} is not a valid Ernie4_5_VL model.")
             else:
                 from fastdeploy.input.ernie_vl_processor import ErnieMoEVLProcessor
 

fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py

@@ -42,6 +42,7 @@ from fastdeploy.model_executor.models.ernie4_5_moe import (
     Ernie4_5_MLP,
 )
 from fastdeploy.model_executor.models.model_base import ModelForCasualLM
+from fastdeploy.multimodal.registry import MultimodalRegistry
 from fastdeploy.platforms import current_platform
 
 if current_platform.is_cuda():
@@ -487,6 +488,7 @@ class Ernie4_5_VLModel(nn.Layer):
         return out
 
 
+@MultimodalRegistry.register_model()
 class Ernie4_5_VLMoeForConditionalGeneration(ModelForCasualLM):
     """
     Ernie4_5_VLMoeForConditionalGeneration

fastdeploy/multimodal/registry.py

@@ -0,0 +1,49 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from typing import Callable
+
+
+class MultimodalRegistry:
+    """
+    A registry for multimodal models
+    """
+
+    mm_models: set[str] = set()
+
+    @classmethod
+    def register_model(cls, name: str = "") -> Callable:
+        """
+        Register model with the given name, class name is used if name is not provided.
+        """
+
+        def _register(model):
+            nonlocal name
+            if len(name) == 0:
+                name = model.__name__
+            if name in cls.mm_models:
+                raise ValueError(f"multimodal model {name} is already registered")
+            cls.mm_models.add(name)
+            return model
+
+        return _register
+
+    @classmethod
+    def contains_model(cls, name: str) -> bool:
+        """
+        Check if the given name exists in registry.
+        """
+        return name in cls.mm_models

fastdeploy/utils.py

@@ -596,6 +596,24 @@ def version():
     return content
 
 
+class DeprecatedOptionWarning(argparse.Action):
+    def __init__(self, option_strings, dest, **kwargs):
+        super().__init__(option_strings, dest, nargs=0, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        console_logger.warning(f"Deprecated option is detected: {option_string}, which may be removed later")
+        setattr(namespace, self.dest, True)
+
+
+DEPRECATED_ARGS = ["enable_mm"]
+
+
+def deprecated_kwargs_warning(**kwargs):
+    for arg in DEPRECATED_ARGS:
+        if arg in kwargs:
+            console_logger.warning(f"Deprecated argument is detected: {arg}, which may be removed later")
+
+
 llm_logger = get_logger("fastdeploy", "fastdeploy.log")
 data_processor_logger = get_logger("data_processor", "data_processor.log")
 scheduler_logger = get_logger("scheduler", "scheduler.log")