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

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2025-12-11 11:59:39 +08:00 committed by GitHub
parent 5a87d8b9b1
commit 7e24e5d4d6
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GPG Key ID: B5690EEEBB952194
16 changed files with 25 additions and 276 deletions
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2
benchmarks/benchmark_ngram_proposer.py
View File

@ -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(

2
examples/offline_inference/audio_language.py
View File

@ -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(

2
examples/offline_inference/encoder_decoder_multimodal.py
View File

@ -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()

2
examples/offline_inference/qwen2_5_omni/only_thinker.py
View File

@ -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`.",
)

2
examples/offline_inference/qwen3_omni/only_thinker.py
View File

@ -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`.",
)

2
examples/offline_inference/vision_language.py
View File

@ -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`.",
)

6
examples/offline_inference/vision_language_multi_image.py
View File

@ -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(

2
examples/pooling/plugin/prithvi_geospatial_mae_client.py
View File

@ -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

6
examples/pooling/pooling/vision_language_pooling.py
View File

@ -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()

2
tests/conftest.py
View File

@ -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,

58
tests/test_config.py
View File

@ -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"),
[

4
tests/utils.py
View File

@ -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:

131
vllm/config/model.py
View File

@ -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:

73
vllm/engine/arg_utils.py
View File

@ -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"]
)
@ -1192,62 +1177,20 @@ 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",
if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
logger.warning(
"The global random seed is set to %d. Since "
"VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
"affect the random state of the Python process that "
"launched vLLM.",
self.seed,
)
self.seed = 0
if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
logger.warning(
"The global random seed is set to %d. Since "
"VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
"affect the random state of the Python process that "
"launched vLLM.",
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,

2
vllm/entrypoints/llm.py
View File

@ -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,

5
vllm/envs.py
View File

@ -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",