[V1] Get supported tasks from model runner instead of model config (#21585)

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

vllm/entrypoints/llm.py

@@ -14,6 +14,7 @@ from pydantic import ValidationError
 from tqdm.auto import tqdm
 from typing_extensions import TypeVar, deprecated
 
+import vllm.envs as envs
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               BeamSearchSequence,
                               create_sort_beams_key_function)
@@ -44,9 +45,10 @@ from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.outputs import (ClassificationRequestOutput, EmbeddingRequestOutput,
                           PoolingRequestOutput, RequestOutput,
                           ScoringRequestOutput)
-from vllm.pooling_params import PoolingParams, PoolingTask
+from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
                                   RequestOutputKind, SamplingParams)
+from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
                                                get_cached_tokenizer)
 from vllm.usage.usage_lib import UsageContext
@@ -277,6 +279,16 @@ class LLM:
         self.request_counter = Counter()
         self.default_sampling_params: Union[dict[str, Any], None] = None
 
+        if envs.VLLM_USE_V1:
+            supported_tasks = self.llm_engine \
+                .get_supported_tasks()  # type: ignore
+        else:
+            supported_tasks = self.llm_engine.model_config.supported_tasks
+
+        logger.info("Supported_tasks: %s", supported_tasks)
+
+        self.supported_tasks = supported_tasks
+
     def get_tokenizer(
         self,
         lora_request: Optional[LoRARequest] = None,
@@ -1170,8 +1182,7 @@ class LLM:
             A list of `EmbeddingRequestOutput` objects containing the
             embedding vectors in the same order as the input prompts.
         """
-        model_config = self.llm_engine.model_config
+        if "embed" not in self.supported_tasks:
-        if "embed" not in model_config.supported_tasks:
             raise ValueError("Embedding API is not supported by this model. "
                              "Please set `--task embed`.")
 
@@ -1215,8 +1226,7 @@ class LLM:
             A list of `ClassificationRequestOutput` objects containing the
             embedding vectors in the same order as the input prompts.
         """
-        model_config = self.llm_engine.model_config
+        if "classify" not in self.supported_tasks:
-        if "classify" not in model_config.supported_tasks:
             raise ValueError(
                 "Classification API is not supported by this model. "
                 "Please set `--task classify`.")
@@ -1397,8 +1407,8 @@ class LLM:
 
             raise ValueError(" ".join(messages))
 
-        if all(t not in model_config.supported_tasks
+        supported_tasks = self.supported_tasks
-               for t in ("embed", "classify")):
+        if all(t not in supported_tasks for t in ("embed", "classify")):
             raise ValueError("Score API is not supported by this model. "
                              "Please set `--task embed` or `--task classify`.")
 

vllm/entrypoints/openai/api_server.py

@@ -1586,6 +1586,14 @@ async def init_app_state(
     state.vllm_config = vllm_config
     model_config = vllm_config.model_config
 
+    if envs.VLLM_USE_V1:
+        supported_tasks = await engine_client \
+            .get_supported_tasks()  # type: ignore
+    else:
+        supported_tasks = model_config.supported_tasks
+
+    logger.info("Supported_tasks: %s", supported_tasks)
+
     resolved_chat_template = load_chat_template(args.chat_template)
     if resolved_chat_template is not None:
         # Get the tokenizer to check official template
@@ -1647,7 +1655,7 @@ async def init_app_state(
         reasoning_parser=args.reasoning_parser,
         enable_prompt_tokens_details=args.enable_prompt_tokens_details,
         enable_force_include_usage=args.enable_force_include_usage,
-    ) if "generate" in model_config.supported_tasks else None
+    ) if "generate" in supported_tasks else None
     state.openai_serving_chat = OpenAIServingChat(
         engine_client,
         model_config,
@@ -1664,7 +1672,7 @@ async def init_app_state(
         reasoning_parser=args.reasoning_parser,
         enable_prompt_tokens_details=args.enable_prompt_tokens_details,
         enable_force_include_usage=args.enable_force_include_usage,
-    ) if "generate" in model_config.supported_tasks else None
+    ) if "generate" in supported_tasks else None
     state.openai_serving_completion = OpenAIServingCompletion(
         engine_client,
         model_config,
@@ -1673,7 +1681,7 @@ async def init_app_state(
         return_tokens_as_token_ids=args.return_tokens_as_token_ids,
         enable_prompt_tokens_details=args.enable_prompt_tokens_details,
         enable_force_include_usage=args.enable_force_include_usage,
-    ) if "generate" in model_config.supported_tasks else None
+    ) if "generate" in supported_tasks else None
     state.openai_serving_pooling = OpenAIServingPooling(
         engine_client,
         model_config,
@@ -1681,7 +1689,7 @@ async def init_app_state(
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
-    ) if "encode" in model_config.supported_tasks else None
+    ) if "encode" in supported_tasks else None
     state.openai_serving_embedding = OpenAIServingEmbedding(
         engine_client,
         model_config,
@@ -1689,24 +1697,22 @@ async def init_app_state(
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
-    ) if "embed" in model_config.supported_tasks else None
+    ) if "embed" in supported_tasks else None
     state.openai_serving_classification = ServingClassification(
         engine_client,
         model_config,
         state.openai_serving_models,
         request_logger=request_logger,
-    ) if "classify" in model_config.supported_tasks else None
+    ) if "classify" in supported_tasks else None
 
-    enable_serving_reranking = ("classify" in model_config.supported_tasks
+    enable_serving_reranking = ("classify" in supported_tasks and getattr(
-                                and getattr(model_config.hf_config,
+        model_config.hf_config, "num_labels", 0) == 1)
-                                            "num_labels", 0) == 1)
     state.openai_serving_scores = ServingScores(
         engine_client,
         model_config,
         state.openai_serving_models,
         request_logger=request_logger,
-    ) if ("embed" in model_config.supported_tasks
+    ) if ("embed" in supported_tasks or enable_serving_reranking) else None
-          or enable_serving_reranking) else None
 
     state.openai_serving_tokenization = OpenAIServingTokenization(
         engine_client,
@@ -1721,13 +1727,13 @@ async def init_app_state(
         model_config,
         state.openai_serving_models,
         request_logger=request_logger,
-    ) if "transcription" in model_config.supported_tasks else None
+    ) if "transcription" in supported_tasks else None
     state.openai_serving_translation = OpenAIServingTranslation(
         engine_client,
         model_config,
         state.openai_serving_models,
         request_logger=request_logger,
-    ) if "transcription" in model_config.supported_tasks else None
+    ) if "transcription" in supported_tasks else None
     state.task = model_config.task
 
     state.enable_server_load_tracking = args.enable_server_load_tracking

vllm/entrypoints/openai/run_batch.py

@@ -14,6 +14,7 @@ import torch
 from prometheus_client import start_http_server
 from tqdm import tqdm
 
+import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs, optional_type
 from vllm.engine.protocol import EngineClient
@@ -335,6 +336,14 @@ async def run_batch(
 
     model_config = vllm_config.model_config
 
+    if envs.VLLM_USE_V1:
+        supported_tasks = await engine_client \
+            .get_supported_tasks()  # type: ignore
+    else:
+        supported_tasks = model_config.supported_tasks
+
+    logger.info("Supported_tasks: %s", supported_tasks)
+
     # Create the openai serving objects.
     openai_serving_models = OpenAIServingModels(
         engine_client=engine_client,
@@ -351,7 +360,7 @@ async def run_batch(
         chat_template=None,
         chat_template_content_format="auto",
         enable_prompt_tokens_details=args.enable_prompt_tokens_details,
-    ) if "generate" in model_config.supported_tasks else None
+    ) if "generate" in supported_tasks else None
     openai_serving_embedding = OpenAIServingEmbedding(
         engine_client,
         model_config,
@@ -359,19 +368,17 @@ async def run_batch(
         request_logger=request_logger,
         chat_template=None,
         chat_template_content_format="auto",
-    ) if "embed" in model_config.supported_tasks else None
+    ) if "embed" in supported_tasks else None
 
-    enable_serving_reranking = ("classify" in model_config.supported_tasks
+    enable_serving_reranking = ("classify" in supported_tasks and getattr(
-                                and getattr(model_config.hf_config,
+        model_config.hf_config, "num_labels", 0) == 1)
-                                            "num_labels", 0) == 1)
 
     openai_serving_scores = ServingScores(
         engine_client,
         model_config,
         openai_serving_models,
         request_logger=request_logger,
-    ) if ("embed" in model_config.supported_tasks
+    ) if ("embed" in supported_tasks or enable_serving_reranking) else None
-          or enable_serving_reranking) else None
 
     tracker = BatchProgressTracker()
     logger.info("Reading batch from %s...", args.input_file)

vllm/executor/executor_base.py

@@ -16,8 +16,8 @@ from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import ExecuteModelRequest, PoolerOutput
+from vllm.tasks import SupportedTask
 from vllm.utils import make_async
 from vllm.worker.worker_base import WorkerBase
 
@@ -136,9 +136,9 @@ class ExecutorBase(ABC):
         return self.collective_rpc(rpc_func)
 
     @cached_property  # Avoid unnecessary RPC calls
-    def supported_pooling_tasks(self) -> tuple[PoolingTask, ...]:
+    def supported_tasks(self) -> tuple[SupportedTask, ...]:
-        output = self.collective_rpc("get_supported_pooling_tasks")
+        output = self.collective_rpc("get_supported_tasks")
-        return tuple({task for tasks in output for task in tasks})
+        return output[0]
 
     def execute_model(
         self, execute_model_req: ExecuteModelRequest

vllm/model_executor/layers/pooler.py

@@ -16,8 +16,9 @@ from vllm.config import ModelConfig, PoolerConfig
 from vllm.model_executor.pooling_metadata import (  # noqa: E501
     PoolingMetadata as V0PoolingMetadata)
 from vllm.model_executor.pooling_metadata import PoolingTensors
-from vllm.pooling_params import PoolingParams, PoolingTask
+from vllm.pooling_params import PoolingParams
 from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
+from vllm.tasks import PoolingTask
 from vllm.utils import resolve_obj_by_qualname
 from vllm.v1.pool.metadata import PoolingMetadata as V1PoolingMetadata
 

vllm/model_executor/models/bert.py

@@ -26,8 +26,8 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import IntermediateTensors
+from vllm.tasks import PoolingTask
 
 from .interfaces import SupportsCrossEncoding, SupportsQuant, SupportsV0Only
 from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix

vllm/model_executor/models/gritlm.py

@@ -16,8 +16,8 @@ from vllm.model_executor.layers.pooler import (DispatchPooler, Pooler,
                                                get_prompt_token_ids)
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import PoolerOutput
+from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
 from .interfaces import SupportsV0Only

vllm/model_executor/models/modernbert.py

@@ -23,8 +23,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import IntermediateTensors
+from vllm.tasks import PoolingTask
 
 from .interfaces import SupportsCrossEncoding, SupportsV0Only
 from .utils import WeightsMapper, maybe_prefix

vllm/pooling_params.py

@@ -1,17 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import TYPE_CHECKING, Literal, Optional
+from typing import TYPE_CHECKING, Optional
 
 import msgspec
 
 from vllm.sampling_params import RequestOutputKind
+from vllm.tasks import PoolingTask
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
 
-PoolingTask = Literal["encode", "embed", "classify", "score"]
-
 
 class PoolingParams(
         msgspec.Struct,

vllm/tasks.py

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Literal, get_args
+
+GenerationTask = Literal["generate", "transcription"]
+GENERATION_TASKS = get_args(GenerationTask)
+
+PoolingTask = Literal["encode", "embed", "classify", "score"]
+POOLING_TASKS = get_args(PoolingTask)
+
+SupportedTask = Literal[GenerationTask, PoolingTask]

vllm/v1/engine/async_llm.py

@@ -21,6 +21,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
+from vllm.tasks import SupportedTask
 from vllm.transformers_utils.config import (
     maybe_register_config_serialize_by_value)
 from vllm.transformers_utils.tokenizer import AnyTokenizer
@@ -211,6 +212,9 @@ class AsyncLLM(EngineClient):
         if handler := getattr(self, "output_handler", None):
             handler.cancel()
 
+    async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return await self.engine_core.get_supported_tasks_async()
+
     async def add_request(
         self,
         request_id: str,

vllm/v1/engine/core.py

@@ -23,6 +23,7 @@ from vllm.executor.multiproc_worker_utils import _add_prefix
 from vllm.logger import init_logger
 from vllm.logging_utils.dump_input import dump_engine_exception
 from vllm.lora.request import LoRARequest
+from vllm.tasks import POOLING_TASKS, SupportedTask
 from vllm.transformers_utils.config import (
     maybe_register_config_serialize_by_value)
 from vllm.utils import (bind_process_name, make_zmq_socket,
@@ -195,11 +196,17 @@ class EngineCore:
                      "warmup model) took %.2f seconds"), elapsed)
         return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.model_executor.supported_tasks
+
     def add_request(self, request: EngineCoreRequest):
         """Add request to the scheduler."""
         if pooling_params := request.pooling_params:
-            supported_pooling_tasks = (
+            supported_pooling_tasks = [
-                self.model_executor.supported_pooling_tasks)
+                task for task in self.get_supported_tasks()
+                if task in POOLING_TASKS
+            ]
+
             if pooling_params.task not in supported_pooling_tasks:
                 raise ValueError(f"Unsupported task: {pooling_params.task!r} "
                                  f"Supported tasks: {supported_pooling_tasks}")

vllm/v1/engine/core_client.py

@@ -21,6 +21,7 @@ import zmq.asyncio
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.tasks import SupportedTask
 from vllm.utils import get_open_port, get_open_zmq_inproc_path, make_zmq_socket
 from vllm.v1.engine import (EngineCoreOutputs, EngineCoreRequest,
                             EngineCoreRequestType,
@@ -104,6 +105,9 @@ class EngineCoreClient(ABC):
     def get_output(self) -> EngineCoreOutputs:
         raise NotImplementedError
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        raise NotImplementedError
+
     def add_request(self, request: EngineCoreRequest) -> None:
         raise NotImplementedError
 
@@ -170,6 +174,9 @@ class EngineCoreClient(ABC):
     async def get_output_async(self) -> EngineCoreOutputs:
         raise NotImplementedError
 
+    async def get_supported_tasks_async(self) -> tuple[SupportedTask, ...]:
+        raise NotImplementedError
+
     async def add_request_async(self, request: EngineCoreRequest) -> None:
         raise NotImplementedError
 
@@ -238,6 +245,9 @@ class InprocClient(EngineCoreClient):
         outputs, _ = self.engine_core.step()
         return outputs.get(0) or EngineCoreOutputs()
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.engine_core.get_supported_tasks()
+
     def add_request(self, request: EngineCoreRequest) -> None:
         self.engine_core.add_request(request)
 
@@ -608,6 +618,9 @@ class SyncMPClient(MPClient):
 
         return future.result()
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.call_utility("get_supported_tasks")
+
     def add_request(self, request: EngineCoreRequest) -> None:
         if self.is_dp:
             self.engines_running = True
@@ -802,6 +815,9 @@ class AsyncMPClient(MPClient):
         self._ensure_output_queue_task()
         return await future
 
+    async def get_supported_tasks_async(self) -> tuple[SupportedTask, ...]:
+        return await self.call_utility_async("get_supported_tasks")
+
     async def add_request_async(self, request: EngineCoreRequest) -> None:
         request.client_index = self.client_index
         await self._send_input(EngineCoreRequestType.ADD, request)

vllm/v1/engine/llm_engine.py

@@ -18,6 +18,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
+from vllm.tasks import SupportedTask
 from vllm.transformers_utils.tokenizer_group import (
     TokenizerGroup, init_tokenizer_from_configs)
 from vllm.usage.usage_lib import UsageContext
@@ -176,6 +177,9 @@ class LLMEngine:
     def validate_outputs(cls, outputs, output_type):
         return outputs
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.engine_core.get_supported_tasks()
+
     def abort_request(self, request_ids: list[str]) -> None:
         """Remove request_ids from EngineCore and Detokenizer."""
 

vllm/v1/worker/gpu_model_runner.py

@@ -30,15 +30,17 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaBase
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
-from vllm.model_executor.models.interfaces import is_mixture_of_experts
+from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
-from vllm.model_executor.models.interfaces_base import (VllmModelForPooling,
+                                                   supports_transcription)
-                                                        is_pooling_model)
+from vllm.model_executor.models.interfaces_base import (
+    VllmModelForPooling, is_pooling_model, is_text_generation_model)
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
 from vllm.multimodal.utils import group_mm_inputs_by_modality
-from vllm.pooling_params import PoolingParams, PoolingTask
+from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingType
 from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         GiB_bytes, LazyLoader, check_use_alibi, get_dtype_size,
                         is_pin_memory_available, round_up)
@@ -1153,6 +1155,21 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
     def get_model(self) -> nn.Module:
         return self.model
 
+    def get_supported_generation_tasks(self) -> list[GenerationTask]:
+        model = self.get_model()
+        supported_tasks = list[GenerationTask]()
+
+        if is_text_generation_model(model):
+            supported_tasks.append("generate")
+
+        if supports_transcription(model):
+            if model.supports_transcription_only:
+                return ["transcription"]
+
+            supported_tasks.append("transcription")
+
+        return supported_tasks
+
     def get_supported_pooling_tasks(self) -> list[PoolingTask]:
         model = self.get_model()
         if not is_pooling_model(model):
@@ -1160,6 +1177,16 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         return list(model.pooler.get_supported_tasks())
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        tasks = list[SupportedTask]()
+
+        if self.model_config.runner_type == "generate":
+            tasks.extend(self.get_supported_generation_tasks())
+        if self.model_config.runner_type == "pooling":
+            tasks.extend(self.get_supported_pooling_tasks())
+
+        return tuple(tasks)
+
     def apply_grammar_bitmask(
         self,
         scheduler_output: "SchedulerOutput",

vllm/v1/worker/gpu_worker.py

@@ -23,8 +23,8 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import IntermediateTensors
+from vllm.tasks import SupportedTask
 from vllm.utils import GiB_bytes, MemorySnapshot, memory_profiling
 from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
@@ -320,8 +320,8 @@ class Worker(WorkerBase):
     def get_model(self) -> nn.Module:
         return self.model_runner.get_model()
 
-    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
-        return self.model_runner.get_supported_pooling_tasks()
+        return self.model_runner.get_supported_tasks()
 
     @torch.inference_mode()
     def execute_model(

vllm/v1/worker/tpu_model_runner.py

@@ -27,13 +27,15 @@ from vllm.logger import init_logger
 from vllm.lora.layers import BaseLayerWithLoRA
 from vllm.model_executor.model_loader import get_model_loader
 from vllm.model_executor.model_loader.tpu import TPUModelLoader
-from vllm.model_executor.models.interfaces_base import is_pooling_model
+from vllm.model_executor.models.interfaces import supports_transcription
+from vllm.model_executor.models.interfaces_base import (
+    is_pooling_model, is_text_generation_model)
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargs,
                                     PlaceholderRange)
 from vllm.multimodal.utils import group_mm_inputs_by_modality
-from vllm.pooling_params import PoolingTask
 from vllm.sequence import IntermediateTensors
+from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 from vllm.utils import (LayerBlockType, cdiv, is_pin_memory_available,
                         prev_power_of_2)
 from vllm.v1.attention.backends.pallas import (TPU_STR_DTYPE_TO_TORCH_DTYPE,
@@ -489,6 +491,21 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
     def get_model(self) -> nn.Module:
         return self.model
 
+    def get_supported_generation_tasks(self) -> list[GenerationTask]:
+        model = self.get_model()
+        supported_tasks = list[GenerationTask]()
+
+        if is_text_generation_model(model):
+            supported_tasks.append("generate")
+
+        if supports_transcription(model):
+            if model.supports_transcription_only:
+                return ["transcription"]
+
+            supported_tasks.append("transcription")
+
+        return supported_tasks
+
     def get_supported_pooling_tasks(self) -> list[PoolingTask]:
         model = self.get_model()
         if not is_pooling_model(model):
@@ -496,6 +513,16 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         return list(model.pooler.get_supported_tasks())
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        tasks = list[SupportedTask]()
+
+        if self.model_config.runner_type == "generate":
+            tasks.extend(self.get_supported_generation_tasks())
+        if self.model_config.runner_type == "pooling":
+            tasks.extend(self.get_supported_pooling_tasks())
+
+        return tuple(tasks)
+
     def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
         """
         Generates the KVCacheSpec by parsing the kv cache format from each

vllm/v1/worker/tpu_worker.py

@@ -21,7 +21,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
-from vllm.pooling_params import PoolingTask
+from vllm.tasks import SupportedTask
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
 from vllm.v1.attention.backends.pallas import TPU_HEAD_SIZE_ALIGNMENT
 from vllm.v1.core.sched.output import SchedulerOutput
@@ -282,8 +282,8 @@ class TPUWorker:
     def get_model(self) -> nn.Module:
         return self.model_runner.get_model()
 
-    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
-        return self.model_runner.get_supported_pooling_tasks()
+        return self.model_runner.get_supported_tasks()
 
     def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
         return self.model_runner.get_kv_cache_spec()

vllm/worker/model_runner_base.py

@@ -12,9 +12,11 @@ import torch.nn as nn
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.model_executor.models.interfaces_base import is_pooling_model
+from vllm.model_executor.models.interfaces import supports_transcription
-from vllm.pooling_params import PoolingTask
+from vllm.model_executor.models.interfaces_base import (
+    is_pooling_model, is_text_generation_model)
 from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
+from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 
 if TYPE_CHECKING:
     from vllm.attention import AttentionMetadata
@@ -224,6 +226,21 @@ class ModelRunnerBase(ABC, Generic[T]):
     def get_model(self) -> nn.Module:
         raise NotImplementedError
 
+    def get_supported_generation_tasks(self) -> list[GenerationTask]:
+        model = self.get_model()
+        supported_tasks = list[GenerationTask]()
+
+        if is_text_generation_model(model):
+            supported_tasks.append("generate")
+
+        if supports_transcription(model):
+            if model.supports_transcription_only:
+                return ["transcription"]
+
+            supported_tasks.append("transcription")
+
+        return supported_tasks
+
     def get_supported_pooling_tasks(self) -> list[PoolingTask]:
         model = self.get_model()
         if not is_pooling_model(model):
@@ -231,6 +248,16 @@ class ModelRunnerBase(ABC, Generic[T]):
 
         return list(model.pooler.get_supported_tasks())
 
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        tasks = list[SupportedTask]()
+
+        if self.model_config.runner_type == "generate":
+            tasks.extend(self.get_supported_generation_tasks())
+        if self.model_config.runner_type == "pooling":
+            tasks.extend(self.get_supported_pooling_tasks())
+
+        return tuple(tasks)
+
     def execute_model(
         self,
         model_input: T,