[Deprecation] Remove deprecated task, seed and MM settings (#30397)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

benchmarks/benchmark_ngram_proposer.py

@@ -37,7 +37,7 @@ def benchmark_propose(args):
             tokenizer="facebook/opt-125m",
             tokenizer_mode="auto",
             dtype="auto",
-            seed=None,
+            seed=0,
             trust_remote_code=False,
         )
         proposer = NgramProposer(

examples/offline_inference/audio_language.py

@@ -422,7 +422,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     parser.add_argument(

examples/offline_inference/encoder_decoder_multimodal.py

@@ -77,7 +77,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     return parser.parse_args()

examples/offline_inference/qwen2_5_omni/only_thinker.py

@@ -158,7 +158,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/qwen3_omni/only_thinker.py

@@ -158,7 +158,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/vision_language.py

@@ -2031,7 +2031,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/vision_language_multi_image.py

@@ -1382,7 +1382,7 @@ def run_generate(
     model,
     question: str,
     image_urls: list[str],
-    seed: int | None,
+    seed: int,
     tensor_parallel_size: int | None,
 ):
     req_data = model_example_map[model](question, image_urls)
@@ -1416,7 +1416,7 @@ def run_chat(
     model: str,
     question: str,
     image_urls: list[str],
-    seed: int | None,
+    seed: int,
     tensor_parallel_size: int | None,
 ):
     req_data = model_example_map[model](question, image_urls)
@@ -1494,7 +1494,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     parser.add_argument(

examples/pooling/plugin/prithvi_geospatial_mae_client.py

@@ -16,7 +16,7 @@ import requests
 # - start vllm in serving mode with the below args
 #   --model='christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM'
 #   --model-impl terratorch
-#   --task embed --trust-remote-code
+#   --trust-remote-code
 #   --skip-tokenizer-init --enforce-eager
 #   --io-processor-plugin terratorch_segmentation
 #   --enable-mm-embeds

examples/pooling/pooling/vision_language_pooling.py

@@ -305,7 +305,7 @@ def get_query(modality: QueryModality):
     raise ValueError(msg)
 
 
-def run_encode(model: str, modality: QueryModality, seed: int | None):
+def run_encode(model: str, modality: QueryModality, seed: int):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
@@ -335,7 +335,7 @@ def run_encode(model: str, modality: QueryModality, seed: int | None):
         print("-" * 50)
 
 
-def run_score(model: str, modality: QueryModality, seed: int | None):
+def run_score(model: str, modality: QueryModality, seed: int):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
@@ -390,7 +390,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     return parser.parse_args()

tests/conftest.py

@@ -741,7 +741,7 @@ class VllmRunner:
         tokenizer_name: str | None = None,
         tokenizer_mode: str = "auto",
         trust_remote_code: bool = True,
-        seed: int | None = 0,
+        seed: int = 0,
         max_model_len: int | None = 1024,
         dtype: str = "auto",
         disable_log_stats: bool = True,

tests/test_config.py

@@ -89,64 +89,6 @@ def test_update_config():
         new_config3 = update_config(config3, {"a": "new_value"})
 
 
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("distilbert/distilgpt2", "generate", "none", "generate"),
-        ("intfloat/multilingual-e5-small", "pooling", "none", "embed"),
-        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
-        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "none", "classify"),
-        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "none", "embed"),
-        ("openai/whisper-small", "generate", "none", "transcription"),
-    ],
-)
-def test_auto_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="auto")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("distilbert/distilgpt2", "pooling", "embed", "embed"),
-        ("intfloat/multilingual-e5-small", "pooling", "embed", "embed"),
-        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
-        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "classify", "classify"),
-        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "embed", "embed"),
-        ("openai/whisper-small", "pooling", "embed", "embed"),
-    ],
-)
-def test_score_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="score")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("openai/whisper-small", "generate", "none", "transcription"),
-    ],
-)
-def test_transcription_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="transcription")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
 @pytest.mark.parametrize(
     ("model_id", "expected_runner_type", "expected_convert_type"),
     [

tests/utils.py

@@ -119,7 +119,7 @@ class RemoteOpenAIServer:
         vllm_serve_args: list[str],
         *,
         env_dict: dict[str, str] | None = None,
-        seed: int | None = 0,
+        seed: int = 0,
         auto_port: bool = True,
         max_wait_seconds: float | None = None,
         override_hf_configs: dict[str, Any] | None = None,
@@ -283,7 +283,7 @@ class RemoteOpenAIServerCustom(RemoteOpenAIServer):
         child_process_fxn: Callable[[dict[str, str] | None, str, list[str]], None],
         *,
         env_dict: dict[str, str] | None = None,
-        seed: int | None = 0,
+        seed: int = 0,
         auto_port: bool = True,
         max_wait_seconds: float | None = None,
     ) -> None:

vllm/config/model.py

@@ -73,17 +73,6 @@ logger = init_logger(__name__)
 RunnerOption = Literal["auto", RunnerType]
 ConvertType = Literal["none", "embed", "classify", "reward"]
 ConvertOption = Literal["auto", ConvertType]
-TaskOption = Literal[
-    "auto",
-    "generate",
-    "embedding",
-    "embed",
-    "classify",
-    "score",
-    "reward",
-    "transcription",
-    "draft",
-]
 TokenizerMode = Literal["auto", "hf", "slow", "mistral", "deepseek_v32"]
 ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 LogprobsMode = Literal[
@@ -93,12 +82,6 @@ HfOverrides = dict[str, Any] | Callable[[PretrainedConfig], PretrainedConfig]
 ModelImpl = Literal["auto", "vllm", "transformers", "terratorch"]
 LayerBlockType = Literal["attention", "linear_attention", "mamba"]
 
-_RUNNER_TASKS: dict[RunnerType, list[TaskOption]] = {
-    "generate": ["generate", "transcription"],
-    "pooling": ["embedding", "embed", "classify", "score", "reward"],
-    "draft": ["draft"],
-}
-
 _RUNNER_CONVERTS: dict[RunnerType, list[ConvertType]] = {
     "generate": [],
     "pooling": ["embed", "classify", "reward"],
@@ -126,12 +109,6 @@ class ModelConfig:
     """Convert the model using adapters defined in
     [vllm.model_executor.models.adapters][]. The most common use case is to
     adapt a text generation model to be used for pooling tasks."""
-    task: TaskOption | None = None
-    """[DEPRECATED] The task to use the model for. If the model supports more
-    than one model runner, this is used to select which model runner to run.
-
-    Note that the model may support other tasks using the same model runner.
-    """
     tokenizer: SkipValidation[str] = None  # type: ignore
     """Name or path of the Hugging Face tokenizer to use. If unspecified, model
     name or path will be used."""
@@ -335,7 +312,6 @@ class ModelConfig:
         ignored_factors = {
             "runner",
             "convert",
-            "task",
             "tokenizer",
             "tokenizer_mode",
             "seed",
@@ -510,97 +486,6 @@ class ModelConfig:
         is_generative_model = registry.is_text_generation_model(architectures, self)
         is_pooling_model = registry.is_pooling_model(architectures, self)
 
-        def _task_to_convert(task: TaskOption) -> ConvertType:
-            if task == "embedding" or task == "embed":
-                return "embed"
-            if task == "classify":
-                return "classify"
-            if task == "reward":
-                logger.warning(
-                    "Pooling models now default support all pooling; "
-                    "you can use it without any settings."
-                )
-                return "embed"
-            if task == "score":
-                new_task = self._get_default_pooling_task(architectures)
-                return "classify" if new_task == "classify" else "embed"
-
-            return "none"
-
-        if self.task is not None:
-            runner: RunnerOption = "auto"
-            convert: ConvertOption = "auto"
-            msg_prefix = (
-                "The 'task' option has been deprecated and will be "
-                "removed in v0.13.0 or v1.0, whichever comes first."
-            )
-            msg_hint = "Please remove this option."
-
-            is_generative_task = self.task in _RUNNER_TASKS["generate"]
-            is_pooling_task = self.task in _RUNNER_TASKS["pooling"]
-
-            if is_generative_model and is_pooling_model:
-                if is_generative_task:
-                    runner = "generate"
-                    convert = "auto"
-                    msg_hint = (
-                        "Please replace this option with `--runner "
-                        "generate` to continue using this model "
-                        "as a generative model."
-                    )
-                elif is_pooling_task:
-                    runner = "pooling"
-                    convert = "auto"
-                    msg_hint = (
-                        "Please replace this option with `--runner "
-                        "pooling` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:  # task == "auto"
-                    pass
-            elif is_generative_model or is_pooling_model:
-                if is_generative_task:
-                    runner = "generate"
-                    convert = "auto"
-                    msg_hint = "Please remove this option"
-                elif is_pooling_task:
-                    runner = "pooling"
-                    convert = _task_to_convert(self.task)
-                    msg_hint = (
-                        "Please replace this option with `--convert "
-                        f"{convert}` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:  # task == "auto"
-                    pass
-            else:
-                # Neither generative nor pooling model - try to convert if possible
-                if is_pooling_task:
-                    runner = "pooling"
-                    convert = _task_to_convert(self.task)
-                    msg_hint = (
-                        "Please replace this option with `--runner pooling "
-                        f"--convert {convert}` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:
-                    debug_info = {
-                        "architectures": architectures,
-                        "is_generative_model": is_generative_model,
-                        "is_pooling_model": is_pooling_model,
-                    }
-                    raise AssertionError(
-                        "The model should be a generative or "
-                        "pooling model when task is set to "
-                        f"{self.task!r}. Found: {debug_info}"
-                    )
-
-            self.runner = runner
-            self.convert = convert
-
-            msg = f"{msg_prefix} {msg_hint}"
-            warnings.warn(msg, DeprecationWarning, stacklevel=2)
-
         self.runner_type = self._get_runner_type(architectures, self.runner)
         self.convert_type = self._get_convert_type(
             architectures, self.runner_type, self.convert
@@ -918,22 +803,6 @@ class ModelConfig:
 
         return convert_type
 
-    def _get_default_pooling_task(
-        self,
-        architectures: list[str],
-    ) -> Literal["embed", "classify", "reward"]:
-        if self.registry.is_cross_encoder_model(architectures, self):
-            return "classify"
-
-        for arch in architectures:
-            match = try_match_architecture_defaults(arch, runner_type="pooling")
-            if match:
-                _, (_, convert_type) = match
-                assert convert_type != "none"
-                return convert_type
-
-        return "embed"
-
     def _parse_quant_hf_config(self, hf_config: PretrainedConfig):
         quant_cfg = getattr(hf_config, "quantization_config", None)
         if quant_cfg is None:

vllm/engine/arg_utils.py

@@ -71,7 +71,6 @@ from vllm.config.model import (
     LogprobsMode,
     ModelDType,
     RunnerOption,
-    TaskOption,
     TokenizerMode,
 )
 from vllm.config.multimodal import MMCacheType, MMEncoderTPMode
@@ -360,7 +359,6 @@ class EngineArgs:
     hf_config_path: str | None = ModelConfig.hf_config_path
     runner: RunnerOption = ModelConfig.runner
     convert: ConvertOption = ModelConfig.convert
-    task: TaskOption | None = ModelConfig.task
     skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
     enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
     tokenizer_mode: TokenizerMode | str = ModelConfig.tokenizer_mode
@@ -373,7 +371,7 @@ class EngineArgs:
     config_format: str = ModelConfig.config_format
     dtype: ModelDType = ModelConfig.dtype
     kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
-    seed: int | None = 0
+    seed: int = ModelConfig.seed
     max_model_len: int | None = ModelConfig.max_model_len
     cudagraph_capture_sizes: list[int] | None = (
         CompilationConfig.cudagraph_capture_sizes
@@ -462,7 +460,6 @@ class EngineArgs:
         MultiModalConfig, "media_io_kwargs"
     )
     mm_processor_kwargs: dict[str, Any] | None = MultiModalConfig.mm_processor_kwargs
-    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
     mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
     mm_processor_cache_type: MMCacheType | None = (
         MultiModalConfig.mm_processor_cache_type
@@ -558,9 +555,6 @@ class EngineArgs:
     use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
     pt_load_map_location: str = LoadConfig.pt_load_map_location
 
-    # DEPRECATED
-    enable_multimodal_encoder_data_parallel: bool = False
-
     logits_processors: list[str | type[LogitsProcessor]] | None = (
         ModelConfig.logits_processors
     )
@@ -628,7 +622,6 @@ class EngineArgs:
             model_group.add_argument("--model", **model_kwargs["model"])
         model_group.add_argument("--runner", **model_kwargs["runner"])
         model_group.add_argument("--convert", **model_kwargs["convert"])
-        model_group.add_argument("--task", **model_kwargs["task"], deprecated=True)
         model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
         model_group.add_argument("--tokenizer-mode", **model_kwargs["tokenizer_mode"])
         model_group.add_argument(
@@ -882,11 +875,6 @@ class EngineArgs:
         parallel_group.add_argument(
             "--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]
         )
-        parallel_group.add_argument(
-            "--enable-multimodal-encoder-data-parallel",
-            action="store_true",
-            deprecated=True,
-        )
 
         # KV cache arguments
         cache_kwargs = get_kwargs(CacheConfig)
@@ -960,9 +948,6 @@ class EngineArgs:
         multimodal_group.add_argument(
             "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]
         )
-        multimodal_group.add_argument(
-            "--disable-mm-preprocessor-cache", action="store_true", deprecated=True
-        )
         multimodal_group.add_argument(
             "--mm-processor-cache-type", **multimodal_kwargs["mm_processor_cache_type"]
         )
@@ -1192,17 +1177,6 @@ class EngineArgs:
         if is_gguf(self.model):
             self.quantization = self.load_format = "gguf"
 
-        # NOTE(woosuk): In V1, we use separate processes for workers (unless
-        # VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here
-        # doesn't affect the user process.
-        if self.seed is None:
-            logger.warning_once(
-                "`seed=None` is equivalent to `seed=0` in V1 Engine. "
-                "You will no longer be allowed to pass `None` in v0.13.",
-                scope="local",
-            )
-
-            self.seed = 0
         if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
             logger.warning(
                 "The global random seed is set to %d. Since "
@@ -1212,42 +1186,11 @@ class EngineArgs:
                 self.seed,
             )
 
-        if self.disable_mm_preprocessor_cache:
-            logger.warning_once(
-                "`--disable-mm-preprocessor-cache` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-processor-cache-gb 0` instead.",
-                scope="local",
-            )
-
-            self.mm_processor_cache_gb = 0
-        elif envs.VLLM_MM_INPUT_CACHE_GIB != 4:
-            logger.warning_once(
-                "VLLM_MM_INPUT_CACHE_GIB` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-processor-cache-gb %d` instead.",
-                envs.VLLM_MM_INPUT_CACHE_GIB,
-                scope="local",
-            )
-
-            self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB
-
-        if self.enable_multimodal_encoder_data_parallel:
-            logger.warning_once(
-                "--enable-multimodal-encoder-data-parallel` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-encoder-tp-mode data` instead.",
-                scope="local",
-            )
-
-            self.mm_encoder_tp_mode = "data"
-
         return ModelConfig(
             model=self.model,
             hf_config_path=self.hf_config_path,
             runner=self.runner,
             convert=self.convert,
-            task=self.task,
             tokenizer=self.tokenizer,
             tokenizer_mode=self.tokenizer_mode,
             trust_remote_code=self.trust_remote_code,

vllm/entrypoints/llm.py

@@ -198,7 +198,7 @@ class LLM:
         quantization: QuantizationMethods | None = None,
         revision: str | None = None,
         tokenizer_revision: str | None = None,
-        seed: int | None = None,
+        seed: int = 0,
         gpu_memory_utilization: float = 0.9,
         swap_space: float = 4,
         cpu_offload_gb: float = 0,

vllm/envs.py

@@ -72,7 +72,6 @@ if TYPE_CHECKING:
     VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
     VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
     VLLM_MEDIA_CONNECTOR: str = "http"
-    VLLM_MM_INPUT_CACHE_GIB: int = 4
     VLLM_TARGET_DEVICE: str = "cuda"
     VLLM_MAIN_CUDA_VERSION: str = "12.9"
     VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
@@ -786,9 +785,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # imported at runtime.
     # If a non-existing backend is used, an AssertionError will be thrown.
     "VLLM_MEDIA_CONNECTOR": lambda: os.getenv("VLLM_MEDIA_CONNECTOR", "http"),
-    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
-    # Default is 4 GiB per API process + 4 GiB per engine core process
-    "VLLM_MM_INPUT_CACHE_GIB": lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
     # Path to the XLA persistent cache directory.
     # Only used for XLA devices such as TPUs.
     "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
@@ -1681,7 +1677,6 @@ def compile_factors() -> dict[str, object]:
         "VLLM_MEDIA_CONNECTOR",
         "VLLM_ASSETS_CACHE",
         "VLLM_ASSETS_CACHE_MODEL_CLEAN",
-        "VLLM_MM_INPUT_CACHE_GIB",
         "VLLM_WORKER_MULTIPROC_METHOD",
         "VLLM_ENABLE_V1_MULTIPROCESSING",
         "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE",