Prune Ray v1 non-SPMD code paths

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

.buildkite/test-pipeline.yaml

@@ -280,7 +280,7 @@ steps:
     # split the test to avoid interference
     - pytest -v -s v1/core
     - pytest -v -s v1/executor
-    - pytest -v -s v1/offloading
+    - pytest -v -s v1/kv_offload
     - pytest -v -s v1/sample
     - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker

examples/online_serving/multi_instance_data_parallel.py

@@ -1,15 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
-import threading
 from typing import Optional
 
-from vllm.config import VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
-from vllm.v1.metrics.loggers import AggregatedStatLogger, LoggingStatLogger
 
 """
 To run this example, run the following commands simultaneously with
@@ -25,67 +22,37 @@ send a request to the instance with DP rank 1.
 """
 
 
-def _do_background_logging(engine, interval, stop_event):
-    try:
-        while not stop_event.is_set():
-            asyncio.run(engine.do_log_stats())
-            stop_event.wait(interval)
-    except Exception as e:
-        print(f"vLLM background logging shutdown: {e}")
-        pass
-
-
 async def main():
     engine_args = AsyncEngineArgs(
         model="ibm-research/PowerMoE-3b",
         data_parallel_size=2,
-        tensor_parallel_size=1,
         dtype="auto",
         max_model_len=2048,
         data_parallel_address="127.0.0.1",
         data_parallel_rpc_port=62300,
         data_parallel_size_local=1,
         enforce_eager=True,
-        enable_log_requests=True,
-        disable_custom_all_reduce=True,
     )
 
-    def per_engine_logger_factory(config: VllmConfig, rank: int) -> LoggingStatLogger:
-        return LoggingStatLogger(config, rank)
+    engine_client = AsyncLLMEngine.from_engine_args(engine_args)
 
-    engine_client = AsyncLLMEngine.from_engine_args(
-        engine_args,
-        # Example: Using both regular loggers and aggregated logger
-        stat_loggers=[per_engine_logger_factory, AggregatedStatLogger],
-    )
-    stop_logging_event = threading.Event()
-    logging_thread = threading.Thread(
-        target=_do_background_logging,
-        args=(engine_client, 5, stop_logging_event),
-        daemon=True,
-    )
-    logging_thread.start()
     sampling_params = SamplingParams(
         temperature=0.7,
         top_p=0.9,
         max_tokens=100,
     )
-    num_prompts = 10
-    for i in range(num_prompts):
-        prompt = "Who won the 2004 World Series?"
-        final_output: Optional[RequestOutput] = None
-        async for output in engine_client.generate(
-            prompt=prompt,
-            sampling_params=sampling_params,
-            request_id=f"abcdef-{i}",
-            data_parallel_rank=1,
-        ):
-            final_output = output
-        if final_output:
-            print(final_output.outputs[0].text)
 
-    stop_logging_event.set()
-    logging_thread.join()
+    prompt = "Who won the 2004 World Series?"
+    final_output: Optional[RequestOutput] = None
+    async for output in engine_client.generate(
+        prompt=prompt,
+        sampling_params=sampling_params,
+        request_id="abcdef",
+        data_parallel_rank=1,
+    ):
+        final_output = output
+    if final_output:
+        print(final_output.outputs[0].text)
 
 
 if __name__ == "__main__":

tests/v1/engine/test_async_llm.py

@@ -18,7 +18,7 @@ from vllm.platforms import current_platform
 from vllm.sampling_params import RequestOutputKind
 from vllm.utils import set_default_torch_num_threads
 from vllm.v1.engine.async_llm import AsyncLLM
-from vllm.v1.metrics.loggers import AggregatedStatLogger, LoggingStatLogger
+from vllm.v1.metrics.loggers import LoggingStatLogger
 
 if not current_platform.is_cuda():
     pytest.skip(reason="V1 currently only supported on CUDA.",
@@ -389,15 +389,6 @@ class MockLoggingStatLogger(LoggingStatLogger):
         self.log = MagicMock()
 
 
-class MockAggregatedStatLogger(AggregatedStatLogger):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 engine_indexes: Optional[list[int]] = None):
-        super().__init__(vllm_config, engine_indexes)
-        self.log = MagicMock()
-
-
 @pytest.mark.asyncio
 async def test_customize_loggers(monkeypatch):
     """Test that we can customize the loggers.
@@ -424,35 +415,6 @@ async def test_customize_loggers(monkeypatch):
         stat_loggers[0][0].log.assert_called_once()
 
 
-@pytest.mark.asyncio
-async def test_customize_aggregated_loggers(monkeypatch):
-    """Test that we can customize the aggregated loggers.
-    If a customized logger is provided at the init, it should
-    be added to the default loggers.
-    """
-
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
-        with set_default_torch_num_threads(1):
-            engine = AsyncLLM.from_engine_args(
-                TEXT_ENGINE_ARGS,
-                stat_loggers=[MockLoggingStatLogger, MockAggregatedStatLogger],
-            )
-        after.callback(engine.shutdown)
-
-        await engine.do_log_stats()
-
-        stat_loggers = engine.logger_manager.per_engine_logger_dict
-        assert len(stat_loggers) == 1
-        assert len(
-            stat_loggers[0]) == 2  # LoggingStatLogger + MockLoggingStatLogger
-        aggregated_loggers = engine.logger_manager.aggregated_loggers
-        assert len(aggregated_loggers) == 1
-        aggregated_loggers[0].log.assert_called_once()
-        stat_loggers[0][0].log.assert_called_once()
-
-
 @pytest.mark.asyncio(scope="module")
 async def test_dp_rank_argument(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m, ExitStack() as after:

tests/v1/kv_offload/test_worker.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.v1.offloading.abstract import LoadStoreSpec
-from vllm.v1.offloading.worker.worker import (OffloadingHandler,
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.worker.worker import (OffloadingHandler,
                                               OffloadingWorker, TransferResult,
                                               TransferSpec)
 

tools/check_pickle_imports.py

@@ -50,8 +50,8 @@ ALLOWED_FILES = set([
     # cloudpickle
     'vllm/worker/worker_base.py',
     'vllm/executor/mp_distributed_executor.py',
-    'vllm/executor/ray_distributed_executor.py',
     'vllm/entrypoints/llm.py',
+    'vllm/v1/executor/ray_distributed_executor.py',
     'tests/utils.py',
     # pickle and cloudpickle
     'vllm/utils/__init__.py',

vllm/config/compilation.py

@@ -563,18 +563,6 @@ class CompilationConfig:
                 self.cudagraph_mode = CUDAGraphMode.FULL
             self.splitting_ops = []
 
-        if envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput":
-            # exclude MoE dispatch/combine from capture by ensuring
-            # piecewise splitting includes them, so communication remains
-            # outside CUDA graphs while compute can still be graphed.
-            moe_ops = [
-                "vllm.moe_forward",
-                "vllm.moe_forward_shared",
-            ]
-            for op in moe_ops:
-                if op not in self.splitting_ops:
-                    self.splitting_ops.append(op)
-
     def splitting_ops_contain_attention(self) -> bool:
         return self.splitting_ops is not None and all(
             op in self.splitting_ops for op in self._attention_ops)

vllm/engine/async_timeout.py

@@ -1,173 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Workaround for https://github.com/python/cpython/issues/86296
-#
-# From https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py
-# Licensed under the Apache License (Apache-2.0)
-
-import asyncio
-import enum
-import sys
-from types import TracebackType
-from typing import Any, Optional, Type
-
-if sys.version_info[:2] >= (3, 11):
-    from asyncio import timeout as asyncio_timeout
-else:
-
-    class _State(enum.Enum):
-        INIT = "INIT"
-        ENTER = "ENTER"
-        TIMEOUT = "TIMEOUT"
-        EXIT = "EXIT"
-
-    class Timeout:
-        # Internal class, please don't instantiate it directly
-        # Use timeout() and timeout_at() public factories instead.
-        #
-        # Implementation note: `async with timeout()` is preferred
-        # over `with timeout()`.
-        # While technically the Timeout class implementation
-        # doesn't need to be async at all,
-        # the `async with` statement explicitly points that
-        # the context manager should be used from async function context.
-        #
-        # This design allows to avoid many silly misusages.
-        #
-        # TimeoutError is raised immediately when scheduled
-        # if the deadline is passed.
-        # The purpose is to time out as soon as possible
-        # without waiting for the next await expression.
-
-        __slots__ = ("_deadline", "_loop", "_state", "_timeout_handler")
-
-        def __init__(self, deadline: Optional[float],
-                     loop: asyncio.AbstractEventLoop) -> None:
-            self._loop = loop
-            self._state = _State.INIT
-
-            self._timeout_handler = None  # type: Optional[asyncio.Handle]
-            if deadline is None:
-                self._deadline = None  # type: Optional[float]
-            else:
-                self.update(deadline)
-
-        async def __aenter__(self) -> "Timeout":
-            self._do_enter()
-            return self
-
-        async def __aexit__(
-            self,
-            exc_type: Optional[Type[BaseException]],
-            exc_val: Optional[BaseException],
-            exc_tb: Optional[TracebackType],
-        ) -> Optional[bool]:
-            self._do_exit(exc_type)
-            return None
-
-        @property
-        def expired(self) -> bool:
-            """Is timeout expired during execution?"""
-            return self._state == _State.TIMEOUT
-
-        @property
-        def deadline(self) -> Optional[float]:
-            return self._deadline
-
-        def reject(self) -> None:
-            """Reject scheduled timeout if any."""
-            # cancel is maybe better name but
-            # task.cancel() raises CancelledError in asyncio world.
-            if self._state not in (_State.INIT, _State.ENTER):
-                raise RuntimeError(f"invalid state {self._state.value}")
-            self._reject()
-
-        def _reject(self) -> None:
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-                self._timeout_handler = None
-
-        def shift(self, delay: float) -> None:
-            """Advance timeout on delay seconds.
-            The delay can be negative.
-            Raise RuntimeError if shift is called when deadline is not scheduled
-            """
-            deadline = self._deadline
-            if deadline is None:
-                raise RuntimeError(
-                    "cannot shift timeout if deadline is not scheduled")
-            self.update(deadline + delay)
-
-        def update(self, deadline: float) -> None:
-            """Set deadline to absolute value.
-            deadline argument points on the time in the same clock system
-            as loop.time().
-            If new deadline is in the past the timeout is raised immediately.
-            Please note: it is not POSIX time but a time with
-            undefined starting base, e.g. the time of the system power on.
-            """
-            if self._state == _State.EXIT:
-                raise RuntimeError(
-                    "cannot reschedule after exit from context manager")
-            if self._state == _State.TIMEOUT:
-                raise RuntimeError("cannot reschedule expired timeout")
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-            self._deadline = deadline
-            if self._state != _State.INIT:
-                self._reschedule()
-
-        def _reschedule(self) -> None:
-            assert self._state == _State.ENTER
-            deadline = self._deadline
-            if deadline is None:
-                return
-
-            now = self._loop.time()
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-
-            task = asyncio.current_task()
-            if deadline <= now:
-                self._timeout_handler = self._loop.call_soon(
-                    self._on_timeout, task)
-            else:
-                self._timeout_handler = self._loop.call_at(
-                    deadline, self._on_timeout, task)
-
-        def _do_enter(self) -> None:
-            if self._state != _State.INIT:
-                raise RuntimeError(f"invalid state {self._state.value}")
-            self._state = _State.ENTER
-            self._reschedule()
-
-        def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:
-            if exc_type is asyncio.CancelledError and \
-                    self._state == _State.TIMEOUT:
-                self._timeout_handler = None
-                raise asyncio.TimeoutError
-            # timeout has not expired
-            self._state = _State.EXIT
-            self._reject()
-            return None
-
-        def _on_timeout(self, task: "Optional[asyncio.Task[Any]]") -> None:
-            if task:
-                task.cancel()
-            self._state = _State.TIMEOUT
-            # drop the reference early
-            self._timeout_handler = None
-
-    def asyncio_timeout(delay: Optional[float]) -> Timeout:
-        """timeout context manager.
-        Useful in cases when you want to apply timeout logic around block
-        of code or in cases when asyncio.wait_for is not suitable. For example:
-        >>> async with timeout(0.001):
-        ...     async with aiohttp.get('https://github.com') as r:
-        ...         await r.text()
-        delay - value in seconds or None to disable timeout logic
-        """
-        loop = asyncio.get_running_loop()
-        deadline = loop.time() + delay if delay is not None else None
-        return Timeout(deadline, loop)

vllm/engine/llm_engine.py

@@ -433,9 +433,9 @@ class LLMEngine:
                     f"ExecutorBase. Got {distributed_executor_backend}.")
             executor_class = distributed_executor_backend
         elif distributed_executor_backend == "ray":
-            from vllm.executor.ray_distributed_executor import (
-                RayDistributedExecutor)
-            executor_class = RayDistributedExecutor
+            raise RuntimeError(
+                "The Ray distributed executor is only available in the v1 "
+                "engine. Enable it by setting 'VLLM_USE_V1=1'.")
         elif distributed_executor_backend == "mp":
             from vllm.executor.mp_distributed_executor import (
                 MultiprocessingDistributedExecutor)

vllm/executor/ray_distributed_executor.py

@@ -1,699 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import asyncio
-import os
-from collections import defaultdict
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
-
-import cloudpickle
-import msgspec
-
-import vllm.envs as envs
-from vllm.executor.executor_base import (
-    DistributedExecutorBase)  # yapf: disable
-from vllm.executor.msgspec_utils import encode_hook
-from vllm.executor.ray_utils import (RayWorkerWrapper, initialize_ray_cluster,
-                                     ray)
-from vllm.logger import init_logger
-from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.platforms import current_platform
-from vllm.ray.ray_env import get_env_vars_to_copy
-from vllm.sequence import ExecuteModelRequest
-from vllm.utils import (_run_task_with_lock, get_distributed_init_method,
-                        get_ip, get_open_port, make_async)
-
-if ray is not None:
-    from ray.actor import ActorHandle
-    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
-else:
-    ActorHandle = None
-
-if TYPE_CHECKING:
-    from ray.util.placement_group import PlacementGroup
-
-logger = init_logger(__name__)
-
-
-@dataclass
-class RayWorkerMetaData:
-    """
-    Metadata for a Ray worker.
-    The order of ray worker creation can be random,
-    and we need to reset the rank after creating all workers.
-    """
-    worker: ActorHandle
-    created_rank: int
-    adjusted_rank: int = -1
-    ip: str = ""
-
-
-class RayDistributedExecutor(DistributedExecutorBase):
-    """Ray-based distributed executor"""
-
-    # These env vars are worker-specific, therefore are NOT copied
-    # from the driver to the workers
-    WORKER_SPECIFIC_ENV_VARS = {
-        "VLLM_HOST_IP", "VLLM_HOST_PORT", "LOCAL_RANK", "CUDA_VISIBLE_DEVICES"
-    }
-
-    # These non-vLLM env vars are copied from the driver to workers
-    ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"}
-
-    uses_ray: bool = True
-
-    def _init_executor(self) -> None:
-        self.forward_dag: Optional[ray.dag.CompiledDAG] = None
-        if envs.VLLM_USE_V1:
-            # V1 uses SPMD worker and compiled DAG
-            os.environ["VLLM_USE_RAY_SPMD_WORKER"] = "1"
-            os.environ["VLLM_USE_RAY_COMPILED_DAG"] = "1"
-
-            # For TPU or XPU, avoid compiling NVIDIA's NCCL
-            if current_platform.is_tpu() or current_platform.is_xpu():
-                os.environ["VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE"] = "shm"
-
-        # If the env var is set, it uses the Ray's compiled DAG API
-        # which optimizes the control plane overhead.
-        # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
-        # Currently, this requires USE_RAY_SPMD_WORKER=True.
-        self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
-        # If the env var is set, then we do not distinguish between the
-        # "driver worker" vs other workers. Also, the rank 0 worker will
-        # be executed in a remote Ray worker. Currently this requires
-        # USE_RAY_COMPILED_DAG=True.
-        self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
-        if self.use_ray_compiled_dag:
-            assert self.use_ray_spmd_worker, (
-                "VLLM_USE_RAY_COMPILED_DAG=1 requires "
-                "VLLM_USE_RAY_SPMD_WORKER=1")
-        if self.use_ray_spmd_worker:
-            # TODO: Support SPMD worker for non-DAG Ray executor.
-            assert self.use_ray_compiled_dag, (
-                "VLLM_USE_RAY_SPMD_WORKER=1 requires "
-                "VLLM_USE_RAY_COMPILED_DAG=1")
-
-        assert self.uses_ray
-        initialize_ray_cluster(self.parallel_config)
-        placement_group = self.parallel_config.placement_group
-
-        # Disable Ray usage stats collection.
-        ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
-        if ray_usage != "1":
-            os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
-
-        # Create the parallel GPU workers.
-        self._init_workers_ray(placement_group)
-
-        self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
-        self.output_decoder = msgspec.msgpack.Decoder(
-            Optional[List[SamplerOutput]])
-        self.use_v1 = envs.VLLM_USE_V1
-
-        self.pp_locks: Optional[List[asyncio.Lock]] = None
-        if not self.use_ray_compiled_dag:
-            self.driver_exec_method = make_async(
-                self.driver_worker.execute_method)
-
-    def shutdown(self) -> None:
-        if logger:
-            # Somehow logger can be None here.
-            logger.info(
-                "Shutting down Ray distributed executor. If you see error log "
-                "from logging.cc regarding SIGTERM received, please ignore "
-                "because this is the expected termination process in Ray.")
-        if hasattr(self, "forward_dag") and self.forward_dag is not None:
-            self.forward_dag.teardown()
-            import ray
-            for worker in self.workers:
-                ray.kill(worker)
-            self.forward_dag = None
-
-    def _configure_ray_workers_use_nsight(self,
-                                          ray_remote_kwargs) -> Dict[str, Any]:
-        # If nsight profiling is enabled, we need to set the profiling
-        # configuration for the ray workers as runtime env.
-        runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
-        runtime_env.update({
-            "nsight": {
-                "t": "cuda,cudnn,cublas",
-                "o": "'worker_process_%p'",
-                "cuda-graph-trace": "node",
-            }
-        })
-
-        return ray_remote_kwargs
-
-    # child class could overwrite this to return actual env vars.
-    def _get_env_vars_to_be_updated(self):
-        return self._env_vars_for_all_workers
-
-    def _init_workers_ray(self, placement_group: "PlacementGroup",
-                          **ray_remote_kwargs):
-        num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
-
-        # The driver dummy worker does not actually use any resources.
-        # It holds the resource for the driver worker.
-        self.driver_dummy_worker: Optional[RayWorkerWrapper] = None
-        # The remaining workers are the actual ray actors.
-        self.workers: List[RayWorkerWrapper] = []
-
-        # Used in ray compiled DAG: indexed first by PP rank,
-        # and then TP rank. In other words, the inner list is
-        # the TP group of workers for a PP rank.
-        self.pp_tp_workers: List[List[RayWorkerWrapper]] = []
-
-        if self.parallel_config.ray_workers_use_nsight:
-            ray_remote_kwargs = self._configure_ray_workers_use_nsight(
-                ray_remote_kwargs)
-
-        logger.info("use_ray_spmd_worker: %s", self.use_ray_spmd_worker)
-
-        # Create the workers.
-        bundle_indices: List[int]
-        if envs.VLLM_RAY_BUNDLE_INDICES:
-            # Use the bundle indices specified by the user.
-            bundle_indices = list(
-                map(int, envs.VLLM_RAY_BUNDLE_INDICES.split(",")))
-            assert len(bundle_indices) == self.parallel_config.world_size, \
-            ("VLLM_RAY_BUNDLE_INDICES must have the same size"
-            f" as the world size, but got {bundle_indices=} "
-            f"and {self.parallel_config.world_size=}")
-            assert len(set(bundle_indices)) == len(bundle_indices), \
-            ("VLLM_RAY_BUNDLE_INDICES cannot have duplicate values,"
-            f" but got {bundle_indices=}")
-        else:
-            # use the first N bundles that have GPU resources.
-            bundle_indices = []
-            for bundle_id, bundle in enumerate(placement_group.bundle_specs):
-                if bundle.get(current_platform.ray_device_key, 0):
-                    bundle_indices.append(bundle_id)
-            bundle_indices = bundle_indices[:self.parallel_config.world_size]
-
-        worker_metadata: List[RayWorkerMetaData] = []
-        driver_ip = get_ip()
-        for rank, bundle_id in enumerate(bundle_indices):
-            scheduling_strategy = PlacementGroupSchedulingStrategy(
-                placement_group=placement_group,
-                placement_group_capture_child_tasks=True,
-                placement_group_bundle_index=bundle_id,
-            )
-
-            if current_platform.ray_device_key == "GPU":
-                # NV+AMD GPUs, and Intel XPUs
-                worker = ray.remote(
-                    num_cpus=0,
-                    num_gpus=num_gpus,
-                    scheduling_strategy=scheduling_strategy,
-                    **ray_remote_kwargs,
-                )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
-                                           rpc_rank=rank)
-            else:
-                worker = ray.remote(
-                    num_cpus=0,
-                    num_gpus=0,
-                    resources={current_platform.ray_device_key: num_gpus},
-                    scheduling_strategy=scheduling_strategy,
-                    **ray_remote_kwargs,
-                )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
-                                           rpc_rank=rank)
-            worker_metadata.append(
-                RayWorkerMetaData(worker=worker, created_rank=rank))
-
-        worker_ips = ray.get([
-            each.worker.get_node_ip.remote()  # type: ignore[attr-defined]
-            for each in worker_metadata
-        ])
-
-        for each, ip in zip(worker_metadata, worker_ips):
-            each.ip = ip
-
-        if not self.use_ray_spmd_worker:
-            for i, each in enumerate(worker_metadata):
-                # find and remove the dummy worker from the list
-                worker = each.worker
-                worker_ip = each.ip
-                if self.driver_dummy_worker is None and worker_ip == driver_ip:
-                    # If the worker is on the same node as the driver, we use it
-                    # as the resource holder for the driver process.
-                    self.driver_dummy_worker = worker
-                    self.driver_worker = RayWorkerWrapper(
-                        vllm_config=self.vllm_config, rpc_rank=0)
-                    worker_metadata.pop(i)
-                    break
-
-        logger.debug("workers: %s", worker_metadata)
-        logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
-        if not self.use_ray_spmd_worker and self.driver_dummy_worker is None:
-            raise ValueError(
-                "Ray does not allocate any GPUs on the driver node."
-                f"Driver IP: {driver_ip}, worker IPs: {worker_ips}."
-                "Consider adjusting the Ray placement group or running "
-                "the driver on a GPU node.")
-
-        ip_counts: Dict[str, int] = {}
-        for ip in worker_ips:
-            ip_counts[ip] = ip_counts.get(ip, 0) + 1
-
-        def sort_by_driver_then_worker_ip(item: RayWorkerMetaData):
-            """
-            Sort the workers based on 3 properties:
-            1. If the worker is on the same node as the driver (vllm engine),
-                it should be placed first.
-            2. Then, if the worker is on a node with fewer workers, it should
-                be placed first.
-            3. Finally, if the work is on a node with smaller IP address, it
-                should be placed first.
-            """
-            ip = item.ip
-            return (0 if ip == driver_ip else 1, ip_counts[ip], ip)
-
-        # After sorting, the workers on the same node will be
-        # close to each other, and the workers on the driver
-        # node will be placed first.
-        sorted_worker_metadata = sorted(worker_metadata,
-                                        key=sort_by_driver_then_worker_ip)
-        start_rank = 0 if self.use_ray_spmd_worker else 1
-        for i, item in enumerate(sorted_worker_metadata):
-            item.adjusted_rank = i + start_rank
-        self.workers = [item.worker for item in sorted_worker_metadata]
-        rerank_mapping = {
-            item.created_rank: item.adjusted_rank
-            for item in sorted_worker_metadata
-        }
-        self._run_workers("adjust_rank", rerank_mapping)
-
-        # Get the set of GPU IDs used on each node.
-        worker_node_and_gpu_ids = []
-        for worker in [self.driver_dummy_worker] + self.workers:
-            if worker is None:
-                # driver_dummy_worker can be None when using ray spmd worker.
-                continue
-            worker_node_and_gpu_ids.append(
-                ray.get(worker.get_node_and_gpu_ids.remote()) \
-            ) # type: ignore
-
-        node_workers = defaultdict(list)  # node id -> list of worker ranks
-        node_gpus = defaultdict(list)  # node id -> list of gpu ids
-
-        for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
-            node_workers[node_id].append(i)
-            # `gpu_ids` can be a list of strings or integers.
-            # convert them to integers for consistency.
-            # NOTE: gpu_ids can be larger than 9 (e.g. 16 GPUs),
-            # string sorting is not sufficient.
-            # see https://github.com/vllm-project/vllm/issues/5590
-            gpu_ids = [int(x) for x in gpu_ids]
-            node_gpus[node_id].extend(gpu_ids)
-        for node_id, gpu_ids in node_gpus.items():
-            node_gpus[node_id] = sorted(gpu_ids)
-
-        all_ips = set(worker_ips + [driver_ip])
-        n_ips = len(all_ips)
-        n_nodes = len(node_workers)
-
-        if n_nodes != n_ips:
-            raise RuntimeError(
-                f"Every node should have a unique IP address. Got {n_nodes}"
-                f" nodes with node ids {list(node_workers.keys())} and "
-                f"{n_ips} unique IP addresses {all_ips}. Please check your"
-                " network configuration. If you set `VLLM_HOST_IP`"
-                " environment variable, make sure it is unique for"
-                " each node.")
-
-        # Set environment variables for the driver and workers.
-        all_args_to_update_environment_variables = [{
-            current_platform.device_control_env_var:
-            ",".join(map(str, node_gpus[node_id])),
-        } for (node_id, _) in worker_node_and_gpu_ids]
-
-        # Environment variables to copy from driver to workers
-        env_vars_to_copy = get_env_vars_to_copy(
-            exclude_vars=self.WORKER_SPECIFIC_ENV_VARS,
-            additional_vars=set(current_platform.additional_env_vars).union(
-                self.ADDITIONAL_ENV_VARS),
-            destination="workers")
-
-        # Copy existing env vars to each worker's args
-        for args in all_args_to_update_environment_variables:
-            # TODO: refactor platform-specific env vars
-            for name in env_vars_to_copy:
-                if name in os.environ:
-                    args[name] = os.environ[name]
-
-        self._env_vars_for_all_workers = (
-            all_args_to_update_environment_variables)
-
-        self._run_workers("update_environment_variables",
-                          self._get_env_vars_to_be_updated())
-
-        if len(node_gpus) == 1:
-            # in single node case, we don't need to get the IP address.
-            # the loopback address is sufficient
-            # NOTE: a node may have several IP addresses, one for each
-            # network interface. `get_ip()` might return any of them,
-            # while they might not work for communication inside the node
-            # if the network setup is complicated. Using the loopback address
-            # solves this issue, as it always works for communication inside
-            # the node.
-            driver_ip = "127.0.0.1"
-        distributed_init_method = get_distributed_init_method(
-            driver_ip, get_open_port())
-
-        # Initialize the actual workers inside worker wrapper.
-        all_kwargs = []
-        for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):
-            local_rank = node_workers[node_id].index(rank)
-            kwargs = dict(
-                vllm_config=self.vllm_config,
-                local_rank=local_rank,
-                rank=rank,
-                distributed_init_method=distributed_init_method,
-                is_driver_worker=(not self.parallel_config)
-                or (rank % self.parallel_config.tensor_parallel_size == 0),
-            )
-            all_kwargs.append(kwargs)
-        self._run_workers("init_worker", all_kwargs)
-
-        self._run_workers("init_device")
-        self._run_workers("load_model",
-                          max_concurrent_workers=self.parallel_config.
-                          max_parallel_loading_workers)
-
-        if self.use_ray_spmd_worker:
-            for pp_rank in range(self.parallel_config.pipeline_parallel_size):
-                self.pp_tp_workers.append([])
-                for tp_rank in range(
-                        self.parallel_config.tensor_parallel_size):
-                    # PP=2, TP=4
-                    # pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]]
-                    rank = (pp_rank * self.parallel_config.tensor_parallel_size
-                            ) + tp_rank
-                    assert len(self.pp_tp_workers[pp_rank]) == tp_rank
-                    assert pp_rank < len(self.pp_tp_workers)
-                    self.pp_tp_workers[pp_rank].append(self.workers[rank])
-
-        # This is the list of workers that are rank 0 of each TP group EXCEPT
-        # global rank 0. These are the workers that will broadcast to the
-        # rest of the workers.
-        self.tp_driver_workers: List[RayWorkerWrapper] = []
-        # This is the list of workers that are not drivers and not the first
-        # worker in a TP group. These are the workers that will be
-        # broadcasted to.
-        self.non_driver_workers: List[RayWorkerWrapper] = []
-
-        # Enforce rank order for correct rank to return final output.
-        for index, worker in enumerate(self.workers):
-            # The driver worker is rank 0 and not in self.workers.
-            rank = index + 1
-            if rank % self.parallel_config.tensor_parallel_size == 0:
-                self.tp_driver_workers.append(worker)
-            else:
-                self.non_driver_workers.append(worker)
-
-    def _driver_execute_model(
-        self, execute_model_req: Optional[ExecuteModelRequest]
-    ) -> Optional[List[SamplerOutput]]:
-        """Run execute_model in the driver worker.
-
-        Passing None will cause the driver to stop the model execution
-        loop running in each of the remote workers.
-        """
-        assert not self.use_ray_spmd_worker, (
-            "driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
-        return self.driver_worker.execute_method("execute_model",
-                                                 execute_model_req)
-
-    def execute_model(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
-        if not self.use_ray_spmd_worker:
-            return super().execute_model(execute_model_req)
-
-        if self.forward_dag is None:
-            self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
-
-        if self.use_v1:
-            serialized_data = execute_model_req
-        else:
-            serialized_data = self.input_encoder.encode(execute_model_req)
-        outputs = ray.get(self.forward_dag.execute(serialized_data))
-        if self.use_v1:
-            output = outputs[0]
-        else:
-            output = self.output_decoder.decode(outputs[0])
-        return output
-
-    def _run_workers(
-        self,
-        method: Union[str, Callable],
-        *args,
-        async_run_tensor_parallel_workers_only: bool = False,
-        max_concurrent_workers: Optional[int] = None,
-        **kwargs,
-    ) -> Any:
-        """Runs the given method on all workers. Can be used in the following
-        ways:
-
-        Args:
-        - async_run_tensor_parallel_workers_only: If True the method will be
-          run only in the remote TP workers, not the driver worker.
-          It will also be run asynchronously and return a list of futures
-          rather than blocking on the results.
-        - args/kwargs: All workers share the same args/kwargs
-        """
-        if isinstance(method, str):
-            sent_method = method
-        else:
-            sent_method = cloudpickle.dumps(method)
-        del method
-        if self.use_ray_spmd_worker:
-            assert not async_run_tensor_parallel_workers_only, (
-                "async_run_tensor_parallel_workers_only is not supported for "
-                "spmd mode.")
-
-        if max_concurrent_workers:
-            raise NotImplementedError(
-                "max_concurrent_workers is not supported yet.")
-
-        # Start the ray workers first.
-        ray_workers = self.workers
-        if async_run_tensor_parallel_workers_only:
-            ray_workers = self.non_driver_workers
-        ray_worker_outputs = [
-            worker.execute_method.remote(sent_method, *args, **kwargs)
-            for worker in ray_workers
-        ]
-
-        if async_run_tensor_parallel_workers_only:
-            # Just return futures
-            return ray_worker_outputs
-
-        driver_worker_output = []
-        # In SPMD mode, the driver worker is the same as any other worker,
-        # so we only explicitly execute on the driver worker if using a
-        # non-SPMD worker class.
-        if not self.use_ray_spmd_worker:
-            # Start the driver worker after all the ray workers.
-            driver_worker_output = [
-                self.driver_worker.execute_method(sent_method, *args, **kwargs)
-            ]
-
-        # Get the results of the ray workers.
-        if self.workers:
-            ray_worker_outputs = ray.get(ray_worker_outputs)
-
-        return driver_worker_output + ray_worker_outputs
-
-    def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
-        """Wait for futures returned from _run_workers() with
-        async_run_remote_workers_only to complete."""
-        ray.get(parallel_worker_tasks)
-
-    def _check_ray_cgraph_installation(self):
-        import importlib.metadata
-
-        from packaging import version
-
-        required_version = version.parse("2.43.0")
-        current_version = version.parse(importlib.metadata.version("ray"))
-        if current_version < required_version:
-            raise ValueError(f"Ray version {required_version} is "
-                             f"required, but found {current_version}")
-
-        import importlib.util
-        cgraph_spec = importlib.util.find_spec(
-            "ray.experimental.compiled_dag_ref")
-        if cgraph_spec is None:
-            raise ValueError("Ray Compiled Graph is not installed. "
-                             "Run `pip install ray[cgraph]` to install it.")
-
-        cupy_spec = importlib.util.find_spec("cupy")
-        if (cupy_spec is None
-                and envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE == "nccl"):
-            raise ValueError(
-                "cupy is not installed but required since "
-                "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE is set to 'nccl'. "
-                "Run `pip install ray[cgraph]` and check cupy installation.")
-
-    def _compiled_ray_dag(self, enable_asyncio: bool):
-        assert self.parallel_config.use_ray
-        self._check_ray_cgraph_installation()
-        # Enlarge the default value of "RAY_CGRAPH_get_timeout" to 300 seconds
-        # (it is 10 seconds by default). This is a Ray environment variable to
-        # control the timeout of getting result from a compiled graph execution,
-        # i.e., the distributed execution that includes model forward runs and
-        # intermediate tensor communications, in the case of vllm.
-        # Note: we should set this env var before importing
-        # ray.dag, otherwise it will not take effect.
-        os.environ.setdefault("RAY_CGRAPH_get_timeout", "300")  # noqa: SIM112
-        from ray.dag import InputNode, MultiOutputNode
-        logger.info("RAY_CGRAPH_get_timeout is set to %s",
-                    os.environ["RAY_CGRAPH_get_timeout"])  # noqa: SIM112
-        logger.info("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE = %s",
-                    envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE)
-        logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s",
-                    envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
-
-        channel_type = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
-        if channel_type not in ("auto", "nccl", "shm"):
-            raise ValueError(
-                "Invalid value for VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: "
-                f"{channel_type}. Valid values are: 'auto', 'nccl', or 'shm'.")
-
-        with InputNode() as input_data:
-            # Example DAG: PP=2, TP=4
-            #
-            # For V0:
-            # ExecuteModelRequest -> 0 -> (ExecuteModelReq, IntermediateTensors) -> 4 -> SamplerOutput   # noqa: E501
-            # ExecuteModelRequest -> 1 -> (ExecuteModelReq, IntermediateTensors) -> 5 -> SamplerOutput   # noqa: E501
-            # ExecuteModelRequest -> 2 -> (ExecuteModelReq, IntermediateTensors) -> 6 -> SamplerOutput   # noqa: E501
-            # ExecuteModelRequest -> 3 -> (ExecuteModelReq, IntermediateTensors) -> 7 -> SamplerOutput   # noqa: E501
-            #
-            # For V1:
-            # SchedulerOutput -> 0 -> (SchedulerOutput, IntermediateTensors) -> 4 -> ModelRunnerOutput   # noqa: E501
-            # SchedulerOutput -> 1 -> (SchedulerOutput, IntermediateTensors) -> 5 -> ModelRunnerOutput   # noqa: E501
-            # SchedulerOutput -> 2 -> (SchedulerOutput, IntermediateTensors) -> 6 -> ModelRunnerOutput   # noqa: E501
-            # SchedulerOutput -> 3 -> (SchedulerOutput, IntermediateTensors) -> 7 -> ModelRunnerOutput   # noqa: E501
-
-            # All workers in the first TP group will take in the
-            # ExecuteModelRequest as input.
-            outputs = [input_data for _ in self.pp_tp_workers[0]]
-            for pp_rank, tp_group in enumerate(self.pp_tp_workers):
-                # Each PP worker takes in the output of the previous PP worker,
-                # and the TP group executes in SPMD fashion.
-                if self.use_v1:
-                    outputs = [
-                        worker.execute_model_ray.
-                        bind(  # type: ignore[attr-defined]
-                            outputs[i]) for i, worker in enumerate(tp_group)
-                    ]
-                else:
-                    outputs = [
-                        worker.execute_model_spmd.
-                        bind(  # type: ignore[attr-defined]
-                            outputs[i]) for i, worker in enumerate(tp_group)
-                    ]
-
-                last_pp_rank = len(self.pp_tp_workers) - 1
-                if (pp_rank < last_pp_rank and
-                        envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE != "shm"):
-                    # Specify how intermediate tensors should be passed
-                    # between pp stages, no need to specify for the last
-                    # pp stage or when using shared memory (the default).
-                    transport = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
-                    outputs = [
-                        output.with_tensor_transport(transport=transport)
-                        for output in outputs
-                    ]
-
-            forward_dag = MultiOutputNode(outputs)
-
-        if envs.VLLM_USE_RAY_WRAPPED_PP_COMM:
-            from ray.experimental.channel.accelerator_context import (
-                register_accelerator_context)
-
-            from vllm.distributed.device_communicators.ray_communicator import (
-                RayPPCommunicator)
-            register_accelerator_context(torch_module_name="cuda",
-                                         communicator_cls=RayPPCommunicator)
-            logger.info("Using RayPPCommunicator "
-                        "(which wraps vLLM _PP GroupCoordinator) "
-                        "for Ray Compiled Graph communication.")
-        else:
-            logger.info("Using Ray's NCCL communicator for "
-                        "Ray Compiled Graph communication.")
-
-        return forward_dag.experimental_compile(
-            enable_asyncio=enable_asyncio,
-            _overlap_gpu_communication=envs.
-            VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
-
-    def __del__(self):
-        self.shutdown()
-
-    async def execute_model_async(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
-        if not self.use_ray_spmd_worker:
-            return await super().execute_model_async(execute_model_req)
-
-        if self.forward_dag is None:
-            self.forward_dag = self._compiled_ray_dag(enable_asyncio=True)
-
-        serialized_data = self.input_encoder.encode(execute_model_req)
-        dag_future = await self.forward_dag.execute_async(serialized_data)
-        output = await dag_future[0]
-        return self.output_decoder.decode(output)
-
-    async def _driver_execute_model_async(
-        self,
-        execute_model_req: Optional[ExecuteModelRequest] = None
-    ) -> List[SamplerOutput]:
-        assert not self.use_ray_spmd_worker, (
-            "driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
-        if not self.tp_driver_workers:
-            return await self.driver_exec_method("execute_model",
-                                                 execute_model_req)
-        if self.pp_locks is None:
-            # This locks each pipeline parallel stage so multiple virtual
-            # engines can't execute on the same stage at the same time
-            # We create the locks here to avoid creating them in the constructor
-            # which uses a different asyncio loop.
-            self.pp_locks = [
-                asyncio.Lock()
-                for _ in range(self.parallel_config.pipeline_parallel_size)
-            ]
-
-        tasks = [
-            asyncio.create_task(
-                _run_task_with_lock(self.driver_exec_method, self.pp_locks[0],
-                                    "execute_model", execute_model_req))
-        ]
-        for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
-                                                start=1):
-            tasks.append(
-                asyncio.create_task(
-                    _run_task_with_lock(driver_worker.execute_method.remote,
-                                        self.pp_locks[pp_rank],
-                                        "execute_model", execute_model_req)))
-
-        results = await asyncio.gather(*tasks)
-
-        # Only the last PP stage has the final results.
-        return results[-1]
-
-    async def _start_worker_execution_loop(self):
-        assert not self.use_ray_spmd_worker, (
-            "worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1")
-        coros = [
-            worker.execute_method.remote("start_worker_execution_loop")
-            for worker in self.non_driver_workers
-        ]
-        return await asyncio.gather(*coros)
-
-    def check_health(self) -> None:
-        # Assume that the Ray workers are healthy.
-        # TODO: check the health of the Ray workers
-        return

vllm/model_executor/models/__init__.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal,
-                         SupportsPP, SupportsTranscription, SupportsV0Only,
-                         has_inner_state, supports_lora, supports_multimodal,
-                         supports_pp, supports_transcription, supports_v0_only)
+from .interfaces import (HasInnerState, SupportsLoRA, SupportsMRoPE,
+                         SupportsMultiModal, SupportsPP, SupportsTranscription,
+                         SupportsV0Only, has_inner_state, supports_lora,
+                         supports_mrope, supports_multimodal, supports_pp,
+                         supports_transcription, supports_v0_only)
 from .interfaces_base import (VllmModelForPooling, VllmModelForTextGeneration,
                               is_pooling_model, is_text_generation_model)
 from .registry import ModelRegistry
@@ -21,6 +22,8 @@ __all__ = [
     "supports_lora",
     "SupportsMultiModal",
     "supports_multimodal",
+    "SupportsMRoPE",
+    "supports_mrope",
     "SupportsPP",
     "supports_pp",
     "SupportsTranscription",

vllm/model_executor/models/interfaces.py

@@ -8,6 +8,7 @@ from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
 import numpy as np
 import torch
 from torch import Tensor
+from transformers import PretrainedConfig
 from transformers.models.whisper.tokenization_whisper import LANGUAGES
 from typing_extensions import Self, TypeIs
 
@@ -852,3 +853,70 @@ def supports_eagle3(
     model: Union[type[object], object],
 ) -> Union[TypeIs[type[SupportsEagle3]], TypeIs[SupportsEagle3]]:
     return isinstance(model, SupportsEagle3)
+
+
+@runtime_checkable
+class SupportsMRoPE(Protocol):
+    """The interface required for all models that support M-RoPE."""
+
+    supports_mrope: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports M-RoPE.
+    
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """
+        Get M-RoPE input positions and delta value for this specific model.
+        
+        This method should be implemented by each model that supports M-RoPE
+        to provide model-specific logic for computing input positions.
+        
+        Args:
+            input_tokens: List of input token IDs
+            hf_config: HuggingFace model configuration
+            image_grid_thw: Image grid dimensions (t, h, w)
+            video_grid_thw: Video grid dimensions (t, h, w)
+            second_per_grid_ts: Seconds per grid timestep for videos
+            context_len: Context length
+            seq_len: Sequence length
+            audio_feature_lengths: Audio feature lengths for multimodal models
+            use_audio_in_video: Whether to use audio in video for interleaving
+            
+        Returns:
+            Tuple of (llm_positions, mrope_position_delta)
+            - llm_positions: Tensor of shape [3, num_tokens]
+                with T/H/W positions
+            - mrope_position_delta: Delta for position calculations
+        """
+        ...
+
+
+@overload
+def supports_mrope(model: type[object]) -> TypeIs[type[SupportsMRoPE]]:
+    ...
+
+
+@overload
+def supports_mrope(model: object) -> TypeIs[SupportsMRoPE]:
+    ...
+
+
+def supports_mrope(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsMRoPE]], TypeIs[SupportsMRoPE]]:
+    return isinstance(model, SupportsMRoPE)

vllm/model_executor/models/qwen2_vl.py

@@ -32,7 +32,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange, repeat
-from transformers import AutoConfig, BatchFeature
+from transformers import AutoConfig, BatchFeature, PretrainedConfig
 from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
                                           Qwen2VLProcessor)
 from transformers.models.qwen2_vl.configuration_qwen2_vl import (
@@ -73,7 +73,7 @@ from vllm.transformers_utils.config import uses_mrope
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE,
                          SupportsMultiModal, SupportsPP)
 from .utils import (AutoWeightsLoader, WeightsMapper,
                     init_vllm_registered_model, maybe_prefix,
@@ -1096,7 +1096,7 @@ class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
                                         info=Qwen2VLProcessingInfo,
                                         dummy_inputs=Qwen2VLDummyInputsBuilder)
 class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                      SupportsLoRA, SupportsPP):
+                                      SupportsLoRA, SupportsPP, SupportsMRoPE):
 
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
@@ -1109,6 +1109,118 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             "model.": "language_model.model.",
         })
 
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """Get M-RoPE input positions for Qwen2-VL model."""
+        if image_grid_thw is None:
+            image_grid_thw = []
+        if video_grid_thw is None:
+            video_grid_thw = []
+        if second_per_grid_ts is None:
+            second_per_grid_ts = []
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(hf_config.vision_config,
+                                    "tokens_per_second", 1.0)
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            video_second_per_grid_t = 0.0
+            if remain_images > 0:
+                try:
+                    ed_image = input_tokens.index(image_token_id, st)
+                except ValueError:
+                    ed_image = len(input_tokens) + 1
+            else:
+                ed_image = len(input_tokens) + 1
+            if remain_videos > 0:
+                try:
+                    ed_video = input_tokens.index(video_token_id, st)
+                except ValueError:
+                    ed_video = len(input_tokens) + 1
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_second_per_grid_t = 1.0
+                if second_per_grid_ts:
+                    video_second_per_grid_t = second_per_grid_ts[video_index]
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
+                -1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
+                       tokens_per_second).long().flatten()
+
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+
+        return llm_positions, mrope_position_delta
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -36,7 +36,7 @@ def _extract_data_from_linear_base_module(
     assert m.quant_method.quant_config is not None
 
     w = m.weight
-    ws = m.weight_scale_inv
+    ws = m.weight_scale
     quant_block_size = m.quant_method.quant_config.weight_block_size
 
     assert isinstance(w, torch.Tensor)

vllm/platforms/cuda.py

@@ -191,14 +191,17 @@ class CudaPlatformBase(Platform):
         compilation_config = vllm_config.compilation_config
         if (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput"
                 and parallel_config.data_parallel_size > 1
-                and compilation_config.cudagraph_mode
-                not in [CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE]):
+                and compilation_config.cudagraph_mode != CUDAGraphMode.NONE):
+            # TODO: Piecewise Cuda graph might be enabled
+            # if torch compile cache key issue fixed
+            # See https://github.com/vllm-project/vllm/pull/25093
             logger.info(
-                "Data Parallel with DeepEP high-throughput: using PIECEWISE "
-                "CUDA graphs and excluding MoE ops from capture. Set "
-                "VLLM_ALL2ALL_BACKEND=deepep_low_latency if you need MoE "
-                "graphs captured as well.")
-            compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
+                "Data Parallel: disabling cudagraphs since DP "
+                "with DeepEP high-throughput kernels are not CUDA Graph "
+                "compatible. The DeepEP low-latency kernels are CUDA Graph "
+                "compatible. Set the all_to_all backend to deepep_low_latency "
+                "to use those kernels instead.")
+            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
 
     @classmethod
     def get_current_memory_usage(cls,

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -206,12 +206,11 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         )
 
         if H < MAX_HEADS:
-            # Extract the subsets of the outputs
-            returned_lse = lse[:, :H].contiguous(
-            ) if self.need_to_return_lse_for_decode else lse
             out = out[:, :H]
+            if self.need_to_return_lse_for_decode:
+                lse = lse[:, :H].contiguous()
 
-        return out, returned_lse
+        return out, lse
 
     def _forward_decode(
         self,

vllm/v1/executor/ray_distributed_executor.py

@@ -1,62 +1,165 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import os
+import threading
+from collections import defaultdict
 from concurrent.futures import Future
-from typing import Optional, Union
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
 
+import cloudpickle
+
+import vllm.envs as envs
 from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
-from vllm.executor.ray_distributed_executor import (  # noqa
-    RayDistributedExecutor as RayDistributedExecutorV0)
+from vllm.executor.executor_base import DistributedExecutorBase
+from vllm.executor.msgspec_utils import encode_hook
+from vllm.executor.ray_utils import (RayWorkerWrapper, initialize_ray_cluster,
+                                     ray)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.platforms import current_platform
+from vllm.ray.ray_env import get_env_vars_to_copy
+from vllm.sequence import ExecuteModelRequest
+from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
+                        make_async)
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
 from vllm.v1.executor.abstract import Executor
 from vllm.v1.outputs import ModelRunnerOutput
 
+try:  # msgspec is optional at runtime but required for serialization.
+    import msgspec
+except ImportError:  # pragma: no cover - msgspec is an optional dependency.
+    msgspec = None  # type: ignore
+
+if ray is not None:
+    from ray.actor import ActorHandle
+    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+else:
+    ActorHandle = None
+
+if TYPE_CHECKING:
+    from ray.util.placement_group import PlacementGroup
+
 logger = init_logger(__name__)
 
 
-class FutureWrapper(Future):
-    """A wrapper around Ray output reference to meet the interface
-    of .execute_model(): The top level (core busy loop) expects .result() api 
-    to block and return a single output.
-    
-    If aggregator is provided, the outputs from all workers are aggregated upon 
-    the result() call. If not only the first worker's output is returned.
+@dataclass
+class RayWorkerMetaData:
+    """
+    Metadata for a Ray worker.
+    The order of ray worker creation can be random,
+    and we need to reset the rank after creating all workers.
     """
 
-    def __init__(self, refs, aggregator: Optional[KVOutputAggregator] = None):
+    worker: ActorHandle
+    created_rank: int
+    adjusted_rank: int = -1
+    ip: str = ""
+
+
+class FutureWrapper(Future):
+    """Future compatible wrapper around Ray object references."""
+
+    def __init__(self,
+                 refs,
+                 aggregator: Optional[KVOutputAggregator] = None) -> None:
         super().__init__()
-        self.refs = refs
-        self.aggregator = aggregator
+        self._refs = refs
+        self._aggregator = aggregator
+        # Resolve the Ray object references off-thread so that the driver event
+        # loop is not blocked and Future callbacks fire when the result is
+        # ready.
+        threading.Thread(target=self._resolve, daemon=True).start()
 
-    def result(self, timeout=None):
-        if timeout is not None:
-            raise NotImplementedError("timeout is not supported")
+    def cancel(self) -> bool:  # pragma: no cover - cancellation unsupported.
+        return False
 
-        if self.aggregator is None:
-            return self.refs[0].get()
-
-        outputs = [ref.get() for ref in self.refs]
-        return self.aggregator.aggregate(outputs, output_rank=0)
+    def _resolve(self) -> None:
+        try:
+            if ray is None:
+                raise RuntimeError("Ray is required to resolve distributed "
+                                   "results.")
+            outputs = ray.get(self._refs)
+            if self._aggregator is None:
+                result = outputs[0]
+            else:
+                result = self._aggregator.aggregate(outputs, output_rank=0)
+            self.set_result(result)
+        except BaseException as exc:  # pragma: no cover - Ray errors propagated.
+            self.set_exception(exc)
+        finally:
+            self._refs = None
+            self._aggregator = None
 
 
-class RayDistributedExecutor(RayDistributedExecutorV0, Executor):
-    """Ray distributed executor using Ray Compiled Graphs."""
+class RayDistributedExecutor(DistributedExecutorBase, Executor):
+    """Ray-based distributed executor for the v1 engine."""
 
+    # These env vars are worker-specific, therefore are NOT copied
+    # from the driver to the workers
+    WORKER_SPECIFIC_ENV_VARS = {
+        "VLLM_HOST_IP", "VLLM_HOST_PORT", "LOCAL_RANK", "CUDA_VISIBLE_DEVICES"
+    }
+
+    # These non-vLLM env vars are copied from the driver to workers
+    ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"}
+
+    uses_ray: bool = True
     supports_pp: bool = True
 
     def _init_executor(self) -> None:
-        super()._init_executor()
+        self.forward_dag: Optional[ray.dag.CompiledDAG] = None  # type: ignore
+        # V1 executor always relies on the SPMD worker implementation which in
+        # turn requires the compiled DAG API.
+        os.environ["VLLM_USE_RAY_SPMD_WORKER"] = "1"
+        os.environ["VLLM_USE_RAY_COMPILED_DAG"] = "1"
+
+        # For TPU or XPU, avoid compiling NVIDIA's NCCL
+        if current_platform.is_tpu() or current_platform.is_xpu():
+            os.environ["VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE"] = "shm"
+
+        # These flags configure the worker setup.
+        self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
+        self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
+        if self.use_ray_compiled_dag:
+            assert self.use_ray_spmd_worker, (
+                "VLLM_USE_RAY_COMPILED_DAG=1 requires "
+                "VLLM_USE_RAY_SPMD_WORKER=1")
+        if self.use_ray_spmd_worker:
+            assert self.use_ray_compiled_dag, (
+                "VLLM_USE_RAY_SPMD_WORKER=1 requires "
+                "VLLM_USE_RAY_COMPILED_DAG=1")
+
+        assert self.uses_ray
+        initialize_ray_cluster(self.parallel_config)
+        placement_group = self.parallel_config.placement_group
+
+        # Disable Ray usage stats collection.
+        ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
+        if ray_usage != "1":
+            os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
+
+        # Create the parallel GPU workers.
+        self._init_workers_ray(placement_group)
+
+        # msgspec is only required when compiled DAG is disabled which is not
+        # expected for V1, but initialize the codec for completeness.
+        if msgspec is not None:
+            self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
+            self.output_decoder = msgspec.msgpack.Decoder(
+                Optional[List[SamplerOutput]])
+        else:  # pragma: no cover - msgspec should normally be available.
+            self.input_encoder = None
+            self.output_decoder = None
 
         # KV connector setup
         self.has_connector = self.vllm_config.kv_transfer_config is not None
 
     @property
     def max_concurrent_batches(self) -> int:
-        """Ray distributed executor supports pipeline parallelism,
-        meaning that it allows PP size batches to be executed concurrently.
-        """
+        """Ray distributed executor supports pipeline parallelism."""
         if self.scheduler_config.async_scheduling:
             return 2
         return self.parallel_config.pipeline_parallel_size
@@ -66,43 +169,443 @@ class RayDistributedExecutor(RayDistributedExecutorV0, Executor):
         scheduler_output: SchedulerOutput,
         non_block: bool = False,
     ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
-        """Execute the model on the Ray workers.
+        """Execute the model on the Ray workers."""
 
-        Args:
-            scheduler_output: The scheduler output to execute.
-            non_block: If True, the method will return a Future.
-
-        Returns:
-            The model runner output.
-        """
         # Build the compiled DAG for the first time.
-        if self.forward_dag is None:  # type: ignore
+        if self.forward_dag is None:
             self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
 
-        refs = self.forward_dag.execute(scheduler_output)  # type: ignore
+        refs = self.forward_dag.execute(scheduler_output)
 
         if not self.has_connector:
-            # Get output only from a single worker (output_rank)
-            # When PP is not used, we block here until the result is available.
             if not non_block:
                 return refs[0].get()
-
-            # When PP is used, we return a FutureWrapper immediately so that
-            # the scheduler can yield to the next batch.
             return FutureWrapper(refs)
 
-        # Get output from all workers when connector is present
+        assert self.kv_output_aggregator is not None, (
+            "KVOutputAggregator must be initialized when kv transfer is "
+            "configured")
+
         if not non_block:
-            # Block and get results from all workers
             outputs = [ref.get() for ref in refs]
             return self.kv_output_aggregator.aggregate(outputs)
 
-        # Return a future that will aggregate outputs from all workers
         return FutureWrapper(refs, self.kv_output_aggregator)
 
     def reinitialize_distributed(
             self, reconfig_request: ReconfigureDistributedRequest) -> None:
         self._run_workers("reinitialize_distributed", reconfig_request)
         if reconfig_request.new_data_parallel_rank == \
-        ReconfigureRankType.SHUTDOWN_CURRENT_RANK:
+                ReconfigureRankType.SHUTDOWN_CURRENT_RANK:
             self.shutdown()
+
+    def shutdown(self) -> None:
+        if logger:
+            # Somehow logger can be None here.
+            logger.info(
+                "Shutting down Ray distributed executor. If you see error log "
+                "from logging.cc regarding SIGTERM received, please ignore "
+                "because this is the expected termination process in Ray.")
+        if hasattr(self, "forward_dag") and self.forward_dag is not None:
+            self.forward_dag.teardown()
+            import ray as _ray
+            for worker in self.workers:
+                _ray.kill(worker)
+            self.forward_dag = None
+
+    def _configure_ray_workers_use_nsight(self,
+                                          ray_remote_kwargs) -> Dict[str, Any]:
+        # If nsight profiling is enabled, we need to set the profiling
+        # configuration for the ray workers as runtime env.
+        runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
+        runtime_env.update({
+            "nsight": {
+                "t": "cuda,cudnn,cublas",
+                "o": "'worker_process_%p'",
+                "cuda-graph-trace": "node",
+            }
+        })
+
+        return ray_remote_kwargs
+
+    # child class could overwrite this to return actual env vars.
+    def _get_env_vars_to_be_updated(self):
+        return self._env_vars_for_all_workers
+
+    def _init_workers_ray(self, placement_group: "PlacementGroup",
+                          **ray_remote_kwargs):
+        num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
+
+        # Ray actors that perform all model execution.
+        self.workers: List[RayWorkerWrapper] = []
+
+        # Used in ray compiled DAG: indexed first by PP rank,
+        # and then TP rank. In other words, the inner list is
+        # the TP group of workers for a PP rank.
+        self.pp_tp_workers: List[List[RayWorkerWrapper]] = []
+
+        if self.parallel_config.ray_workers_use_nsight:
+            ray_remote_kwargs = self._configure_ray_workers_use_nsight(
+                ray_remote_kwargs)
+
+        logger.info("use_ray_spmd_worker: %s", self.use_ray_spmd_worker)
+
+        # Create the workers.
+        bundle_indices: List[int]
+        if envs.VLLM_RAY_BUNDLE_INDICES:
+            # Use the bundle indices specified by the user.
+            bundle_indices = list(
+                map(int, envs.VLLM_RAY_BUNDLE_INDICES.split(",")))
+            assert len(bundle_indices) == self.parallel_config.world_size, \
+                ("VLLM_RAY_BUNDLE_INDICES must have the same size"
+                 f" as the world size, but got {bundle_indices=} "
+                 f"and {self.parallel_config.world_size=}")
+            assert len(set(bundle_indices)) == len(bundle_indices), \
+                ("VLLM_RAY_BUNDLE_INDICES cannot have duplicate values,"
+                 f" but got {bundle_indices=}")
+        else:
+            # use the first N bundles that have GPU resources.
+            bundle_indices = []
+            for bundle_id, bundle in enumerate(placement_group.bundle_specs):
+                if bundle.get(current_platform.ray_device_key, 0):
+                    bundle_indices.append(bundle_id)
+            bundle_indices = bundle_indices[:self.parallel_config.world_size]
+
+        worker_metadata: List[RayWorkerMetaData] = []
+        driver_ip = get_ip()
+        for rank, bundle_id in enumerate(bundle_indices):
+            scheduling_strategy = PlacementGroupSchedulingStrategy(
+                placement_group=placement_group,
+                placement_group_capture_child_tasks=True,
+                placement_group_bundle_index=bundle_id,
+            )
+
+            if current_platform.ray_device_key == "GPU":
+                # NV+AMD GPUs, and Intel XPUs
+                worker = ray.remote(
+                    num_cpus=0,
+                    num_gpus=num_gpus,
+                    scheduling_strategy=scheduling_strategy,
+                    **ray_remote_kwargs,
+                )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
+                                           rpc_rank=rank)
+            else:
+                worker = ray.remote(
+                    num_cpus=0,
+                    num_gpus=0,
+                    resources={current_platform.ray_device_key: num_gpus},
+                    scheduling_strategy=scheduling_strategy,
+                    **ray_remote_kwargs,
+                )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
+                                           rpc_rank=rank)
+            worker_metadata.append(
+                RayWorkerMetaData(worker=worker, created_rank=rank))
+
+        worker_ips = ray.get([
+            each.worker.get_node_ip.remote()  # type: ignore[attr-defined]
+            for each in worker_metadata
+        ])
+
+        for each, ip in zip(worker_metadata, worker_ips):
+            each.ip = ip
+
+        logger.debug("workers: %s", worker_metadata)
+
+        ip_counts: Dict[str, int] = {}
+        for ip in worker_ips:
+            ip_counts[ip] = ip_counts.get(ip, 0) + 1
+
+        def sort_by_driver_then_worker_ip(item: RayWorkerMetaData):
+            """
+            Sort the workers based on 3 properties:
+            1. If the worker is on the same node as the driver (vllm engine),
+                it should be placed first.
+            2. Then, if the worker is on a node with fewer workers, it should
+                be placed first.
+            3. Finally, if the work is on a node with smaller IP address, it
+                should be placed first.
+            """
+
+            ip = item.ip
+            return (0 if ip == driver_ip else 1, ip_counts[ip], ip)
+
+        # After sorting, the workers on the same node will be
+        # close to each other, and the workers on the driver
+        # node will be placed first.
+        sorted_worker_metadata = sorted(worker_metadata,
+                                        key=sort_by_driver_then_worker_ip)
+        start_rank = 0
+        for i, item in enumerate(sorted_worker_metadata):
+            item.adjusted_rank = i + start_rank
+        self.workers = [item.worker for item in sorted_worker_metadata]
+        rerank_mapping = {
+            item.created_rank: item.adjusted_rank
+            for item in sorted_worker_metadata
+        }
+        self._run_workers("adjust_rank", rerank_mapping)
+
+        # Get the set of GPU IDs used on each node.
+        worker_node_and_gpu_ids = []
+        for worker in self.workers:
+            worker_node_and_gpu_ids.append(
+                ray.get(worker.get_node_and_gpu_ids.remote())
+            )  # type: ignore
+
+        node_workers = defaultdict(list)  # node id -> list of worker ranks
+        node_gpus = defaultdict(list)  # node id -> list of gpu ids
+
+        for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
+            node_workers[node_id].append(i)
+            # `gpu_ids` can be a list of strings or integers.
+            # convert them to integers for consistency.
+            # NOTE: gpu_ids can be larger than 9 (e.g. 16 GPUs),
+            # string sorting is not sufficient.
+            # see https://github.com/vllm-project/vllm/issues/5590
+            gpu_ids = [int(x) for x in gpu_ids]
+            node_gpus[node_id].extend(gpu_ids)
+        for node_id, gpu_ids in node_gpus.items():
+            node_gpus[node_id] = sorted(gpu_ids)
+
+        all_ips = set(worker_ips + [driver_ip])
+        n_ips = len(all_ips)
+        n_nodes = len(node_workers)
+
+        if n_nodes != n_ips:
+            raise RuntimeError(
+                f"Every node should have a unique IP address. Got {n_nodes}"
+                f" nodes with node ids {list(node_workers.keys())} and "
+                f"{n_ips} unique IP addresses {all_ips}. Please check your"
+                " network configuration. If you set `VLLM_HOST_IP`"
+                " environment variable, make sure it is unique for"
+                " each node.")
+
+        # Set environment variables for the driver and workers.
+        all_args_to_update_environment_variables = [{
+            current_platform.device_control_env_var:
+            ",".join(map(str, node_gpus[node_id])),
+        } for (node_id, _) in worker_node_and_gpu_ids]
+
+        # Environment variables to copy from driver to workers
+        env_vars_to_copy = get_env_vars_to_copy(
+            exclude_vars=self.WORKER_SPECIFIC_ENV_VARS,
+            additional_vars=set(current_platform.additional_env_vars).union(
+                self.ADDITIONAL_ENV_VARS),
+            destination="workers")
+
+        # Copy existing env vars to each worker's args
+        for args in all_args_to_update_environment_variables:
+            # TODO: refactor platform-specific env vars
+            for name in env_vars_to_copy:
+                if name in os.environ:
+                    args[name] = os.environ[name]
+
+        self._env_vars_for_all_workers = (
+            all_args_to_update_environment_variables)
+
+        self._run_workers("update_environment_variables",
+                          self._get_env_vars_to_be_updated())
+
+        if len(node_gpus) == 1:
+            # in single node case, we don't need to get the IP address.
+            # the loopback address is sufficient
+            # NOTE: a node may have several IP addresses, one for each
+            # network interface. `get_ip()` might return any of them,
+            # while they might not work for communication inside the node
+            # if the network setup is complicated. Using the loopback address
+            # solves this issue, as it always works for communication inside
+            # the node.
+            driver_ip = "127.0.0.1"
+        distributed_init_method = get_distributed_init_method(
+            driver_ip, get_open_port())
+
+        # Initialize the actual workers inside worker wrapper.
+        all_kwargs = []
+        for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):
+            local_rank = node_workers[node_id].index(rank)
+            kwargs = dict(
+                vllm_config=self.vllm_config,
+                local_rank=local_rank,
+                rank=rank,
+                distributed_init_method=distributed_init_method,
+                is_driver_worker=(not self.parallel_config)
+                or (rank % self.parallel_config.tensor_parallel_size == 0),
+            )
+            all_kwargs.append(kwargs)
+        self._run_workers("init_worker", all_kwargs)
+
+        self._run_workers("init_device")
+        self._run_workers("load_model",
+                          max_concurrent_workers=self.parallel_config.
+                          max_parallel_loading_workers)
+
+        for pp_rank in range(self.parallel_config.pipeline_parallel_size):
+            self.pp_tp_workers.append([])
+            for tp_rank in range(self.parallel_config.tensor_parallel_size):
+                # PP=2, TP=4
+                # pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]]
+                rank = (pp_rank * self.parallel_config.tensor_parallel_size
+                        ) + tp_rank
+                assert len(self.pp_tp_workers[pp_rank]) == tp_rank
+                assert pp_rank < len(self.pp_tp_workers)
+                self.pp_tp_workers[pp_rank].append(self.workers[rank])
+
+    def _driver_execute_model(
+        self, execute_model_req: Optional[ExecuteModelRequest]
+    ) -> Optional[List[SamplerOutput]]:
+        raise RuntimeError(
+            "RayDistributedExecutor only supports compiled DAG execution "
+            "and does not expose a separate driver worker loop.")
+
+    def _run_workers(
+        self,
+        method: Union[str, Callable],
+        *args,
+        async_run_tensor_parallel_workers_only: bool = False,
+        max_concurrent_workers: Optional[int] = None,
+        **kwargs,
+    ) -> Any:
+        """Runs the given method on all workers."""
+
+        if isinstance(method, str):
+            sent_method = method
+        else:
+            sent_method = cloudpickle.dumps(method)
+        del method
+        if self.use_ray_spmd_worker:
+            assert not async_run_tensor_parallel_workers_only, (
+                "async_run_tensor_parallel_workers_only is not supported for "
+                "spmd mode.")
+
+        if max_concurrent_workers:
+            raise NotImplementedError(
+                "max_concurrent_workers is not supported yet.")
+
+        # Start the ray workers first.
+        ray_worker_outputs = [
+            worker.execute_method.remote(sent_method, *args, **kwargs)
+            for worker in self.workers
+        ]
+
+        if not self.workers:
+            return []
+
+        # Get the results of the ray workers.
+        return ray.get(ray_worker_outputs)
+
+    def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
+        """Wait for futures returned from _run_workers()."""
+        ray.get(parallel_worker_tasks)
+
+    def _check_ray_cgraph_installation(self):
+        import importlib.metadata
+
+        from packaging import version
+
+        required_version = version.parse("2.43.0")
+        current_version = version.parse(importlib.metadata.version("ray"))
+        if current_version < required_version:
+            raise ValueError(f"Ray version {required_version} is "
+                             f"required, but found {current_version}")
+
+        import importlib.util
+        cgraph_spec = importlib.util.find_spec(
+            "ray.experimental.compiled_dag_ref")
+        if cgraph_spec is None:
+            raise ValueError("Ray Compiled Graph is not installed. "
+                             "Run `pip install ray[cgraph]` to install it.")
+
+        cupy_spec = importlib.util.find_spec("cupy")
+        if (cupy_spec is None
+                and envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE == "nccl"):
+            raise ValueError(
+                "cupy is not installed but required since "
+                "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE is set to 'nccl'. "
+                "Run `pip install ray[cgraph]` and check cupy installation.")
+
+    def _compiled_ray_dag(self, enable_asyncio: bool):
+        assert self.parallel_config.use_ray
+        self._check_ray_cgraph_installation()
+        # Enlarge the default value of "RAY_CGRAPH_get_timeout" to 300 seconds
+        os.environ.setdefault("RAY_CGRAPH_get_timeout", "300")  # noqa: SIM112
+        from ray.dag import InputNode, MultiOutputNode
+        logger.info("RAY_CGRAPH_get_timeout is set to %s",
+                    os.environ["RAY_CGRAPH_get_timeout"])  # noqa: SIM112
+        logger.info("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE = %s",
+                    envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE)
+        logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s",
+                    envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
+
+        channel_type = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
+        if channel_type not in ("auto", "nccl", "shm"):
+            raise ValueError(
+                "Invalid value for VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: "
+                f"{channel_type}. Valid values are: 'auto', 'nccl', or 'shm'.")
+
+        with InputNode() as input_data:
+            # Example DAG: PP=2, TP=4
+            # SchedulerOutput -> 0 -> (SchedulerOutput, IntermediateTensors) ->
+            # 4 -> ModelRunnerOutput, etc.
+            outputs = [input_data for _ in self.pp_tp_workers[0]]
+            for pp_rank, tp_group in enumerate(self.pp_tp_workers):
+                outputs = [
+                    worker.execute_model_ray.
+                    bind(  # type: ignore[attr-defined]
+                        outputs[i]) for i, worker in enumerate(tp_group)
+                ]
+
+                last_pp_rank = len(self.pp_tp_workers) - 1
+                if (pp_rank < last_pp_rank and
+                        envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE != "shm"):
+                    transport = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
+                    outputs = [
+                        output.with_tensor_transport(transport=transport)
+                        for output in outputs
+                    ]
+
+            forward_dag = MultiOutputNode(outputs)
+
+        if envs.VLLM_USE_RAY_WRAPPED_PP_COMM:
+            from ray.experimental.channel.accelerator_context import (
+                register_accelerator_context)
+
+            from vllm.distributed.device_communicators.ray_communicator import (
+                RayPPCommunicator)
+            register_accelerator_context(torch_module_name="cuda",
+                                         communicator_cls=RayPPCommunicator)
+            logger.info("Using RayPPCommunicator "
+                        "(which wraps vLLM _PP GroupCoordinator) "
+                        "for Ray Compiled Graph communication.")
+        else:
+            logger.info("Using Ray's NCCL communicator for "
+                        "Ray Compiled Graph communication.")
+
+        return forward_dag.experimental_compile(
+            enable_asyncio=enable_asyncio,
+            _overlap_gpu_communication=envs.
+            VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
+
+    def __del__(self):
+        self.shutdown()
+
+    async def execute_model_async(
+            self,
+            scheduler_output: SchedulerOutput) -> ModelRunnerOutput:
+        return await make_async(self.execute_model)(scheduler_output)
+
+    async def _driver_execute_model_async(
+        self,
+        execute_model_req: Optional[ExecuteModelRequest] = None
+    ) -> List[SamplerOutput]:
+        raise RuntimeError(
+            "RayDistributedExecutor only supports compiled DAG execution "
+            "and does not expose a separate driver worker loop.")
+
+    async def _start_worker_execution_loop(self):
+        raise RuntimeError(
+            "RayDistributedExecutor only supports compiled DAG execution "
+            "and does not expose a separate driver worker loop.")
+
+    def check_health(self) -> None:
+        # Assume that the Ray workers are healthy.
+        # TODO: check the health of the Ray workers
+        return

vllm/v1/kv_offload/mediums.py

@@ -4,7 +4,7 @@ from abc import ABC
 
 import numpy as np
 
-from vllm.v1.offloading.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
 
 
 class BlockIDsLoadStoreSpec(LoadStoreSpec, ABC):

vllm/v1/kv_offload/worker/worker.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 
 from vllm.logger import init_logger
-from vllm.v1.offloading.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
 
 # a single transfer spec (src_blocks_spec, dst_blocks_spec)
 TransferSpec = tuple[LoadStoreSpec, LoadStoreSpec]

vllm/v1/metrics/loggers.py

@@ -18,9 +18,7 @@ from vllm.v1.spec_decode.metrics import SpecDecodingLogging, SpecDecodingProm
 
 logger = init_logger(__name__)
 
-PerEngineStatLoggerFactory = Callable[[VllmConfig, int], "StatLoggerBase"]
-StatLoggerFactory = Union[PerEngineStatLoggerFactory,
-                          type["AggregatedStatLoggerBase"]]
+StatLoggerFactory = Callable[[VllmConfig, int], "StatLoggerBase"]
 
 
 class StatLoggerBase(ABC):
@@ -50,16 +48,6 @@ class StatLoggerBase(ABC):
         pass
 
 
-class AggregatedStatLoggerBase(StatLoggerBase):
-    """Abstract base class for loggers that
-       aggregates statistics across multiple engines."""
-
-    @abstractmethod
-    def __init__(self, vllm_config: VllmConfig,
-                 engine_indexes: Optional[list[int]]):
-        ...
-
-
 class LoggingStatLogger(StatLoggerBase):
 
     def __init__(self, vllm_config: VllmConfig, engine_index: int = 0):
@@ -73,7 +61,6 @@ class LoggingStatLogger(StatLoggerBase):
         self.spec_decoding_logging = SpecDecodingLogging()
         self.last_prompt_throughput: float = 0.0
         self.last_generation_throughput: float = 0.0
-        self.engine_is_idle = False
 
     def _reset(self, now):
         self.last_log_time = now
@@ -113,25 +100,25 @@ class LoggingStatLogger(StatLoggerBase):
 
             self.last_scheduler_stats = scheduler_stats
 
-    def get_log_stats(self):
+    def log(self):
         now = time.monotonic()
         prompt_throughput = self._get_throughput(self.num_prompt_tokens, now)
         generation_throughput = self._get_throughput(
             self.num_generation_tokens, now)
 
         self._reset(now)
-        self.last_generation_throughput = generation_throughput
-        self.last_prompt_throughput = prompt_throughput
-        self.engine_is_idle = not any(
-            (prompt_throughput, generation_throughput,
-             self.last_prompt_throughput, self.last_generation_throughput))
 
-    def log(self):
-        self.get_log_stats()
+        scheduler_stats = self.last_scheduler_stats
+
         log_fn = logger.info
-        if self.engine_is_idle:
+        if not any(
+            (prompt_throughput, generation_throughput,
+             self.last_prompt_throughput, self.last_generation_throughput)):
             # Avoid log noise on an idle production system
             log_fn = logger.debug
+        self.last_generation_throughput = generation_throughput
+        self.last_prompt_throughput = prompt_throughput
+
         # Format and print output.
         log_fn(
             "Engine %03d: "
@@ -141,11 +128,11 @@ class LoggingStatLogger(StatLoggerBase):
             "GPU KV cache usage: %.1f%%, "
             "Prefix cache hit rate: %.1f%%",
             self.engine_index,
-            self.last_prompt_throughput,
-            self.last_generation_throughput,
-            self.last_scheduler_stats.num_running_reqs,
-            self.last_scheduler_stats.num_waiting_reqs,
-            self.last_scheduler_stats.kv_cache_usage * 100,
+            prompt_throughput,
+            generation_throughput,
+            scheduler_stats.num_running_reqs,
+            scheduler_stats.num_waiting_reqs,
+            scheduler_stats.kv_cache_usage * 100,
             self.prefix_caching_metrics.hit_rate * 100,
         )
         self.spec_decoding_logging.log(log_fn=log_fn)
@@ -158,61 +145,7 @@ class LoggingStatLogger(StatLoggerBase):
                 self.vllm_config.cache_config.num_gpu_blocks)
 
 
-class AggregatedStatLogger(LoggingStatLogger, AggregatedStatLoggerBase):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 engine_idxs: Optional[list[int]] = None):
-        if engine_idxs is None:
-            engine_idxs = [0]
-        self.engine_idxs = engine_idxs
-        LoggingStatLogger.__init__(self, vllm_config, engine_index=-1)
-
-    def record(
-        self,
-        scheduler_stats: Optional[SchedulerStats],
-        iteration_stats: Optional[IterationStats],
-        engine_idx: int = 0,
-    ):
-        if engine_idx not in self.engine_idxs:
-            logger.warning("Unexpected engine_idx: %d", engine_idx)
-            return
-        LoggingStatLogger.record(self, scheduler_stats, iteration_stats,
-                                 engine_idx)
-
-    def log(self):
-        self.get_log_stats()
-        log_fn = logger.info
-        if self.engine_is_idle:
-            # Avoid log noise on an idle production system
-            log_fn = logger.debug
-        # Format and print output.
-        log_fn(
-            "%s Engines Aggregated: "
-            "Avg prompt throughput: %.1f tokens/s, "
-            "Avg generation throughput: %.1f tokens/s, "
-            "Running: %d reqs, Waiting: %d reqs, "
-            "GPU KV cache usage: %.1f%%, "
-            "Prefix cache hit rate: %.1f%%",
-            len(self.engine_idxs),
-            self.last_prompt_throughput,
-            self.last_generation_throughput,
-            self.last_scheduler_stats.num_running_reqs,
-            self.last_scheduler_stats.num_waiting_reqs,
-            self.last_scheduler_stats.kv_cache_usage * 100,
-            self.prefix_caching_metrics.hit_rate * 100,
-        )
-        self.spec_decoding_logging.log(log_fn=log_fn)
-
-    def log_engine_initialized(self):
-        if self.vllm_config.cache_config.num_gpu_blocks:
-            logger.info(
-                "%d Engines: vllm cache_config_info with initialization "
-                "after num_gpu_blocks is: %d", len(self.engine_idxs),
-                self.vllm_config.cache_config.num_gpu_blocks)
-
-
-class PrometheusStatLogger(AggregatedStatLoggerBase):
+class PrometheusStatLogger(StatLoggerBase):
     _gauge_cls = prometheus_client.Gauge
     _counter_cls = prometheus_client.Counter
     _histogram_cls = prometheus_client.Histogram
@@ -741,32 +674,23 @@ class StatLoggerManager:
 
         # engine_idx: StatLogger
         self.per_engine_logger_dict: dict[int, list[StatLoggerBase]] = {}
-        self.aggregated_loggers: list[AggregatedStatLoggerBase] = []
-
-        aggregated_loggers_factories = set()
+        prometheus_factory = PrometheusStatLogger
         for engine_idx in self.engine_idxs:
             loggers: list[StatLoggerBase] = []
             for logger_factory in factories:
-                # If we get a custom prometheus logger or aggregated logger,
-                # We initialize it separately with all engine idxs.
-                # A custom prometheus logger is typically used for the ray.
-                if (isinstance(logger_factory, type) and issubclass(
-                        logger_factory, AggregatedStatLoggerBase)):
-                    aggregated_loggers_factories.add(logger_factory)
-                else:
-                    loggers.append(logger_factory(vllm_config,
-                                                  engine_idx))  # type: ignore
+                # If we get a custom prometheus logger, use that
+                # instead. This is typically used for the ray case.
+                if (isinstance(logger_factory, type)
+                        and issubclass(logger_factory, PrometheusStatLogger)):
+                    prometheus_factory = logger_factory
+                    continue
+                loggers.append(logger_factory(vllm_config,
+                                              engine_idx))  # type: ignore
             self.per_engine_logger_dict[engine_idx] = loggers
-        # If no custom aggregated logger is provide,
-        # we by default use PrometheusStatLogger
-        if not aggregated_loggers_factories:
-            aggregated_loggers_factories.add(PrometheusStatLogger)
-        # For custom aggregated logger(or default Prometheus Logger)
-        # need to share the metrics between EngineCores.
+
+        # For Prometheus, need to share the metrics between EngineCores.
         # Each EngineCore's metrics are expressed as a unique label.
-        for aggregated_loggers_factory in aggregated_loggers_factories:
-            self.aggregated_loggers.append(
-                aggregated_loggers_factory(vllm_config, engine_idxs))
+        self.prometheus_logger = prometheus_factory(vllm_config, engine_idxs)
 
     def record(
         self,
@@ -780,19 +704,18 @@ class StatLoggerManager:
         per_engine_loggers = self.per_engine_logger_dict[engine_idx]
         for logger in per_engine_loggers:
             logger.record(scheduler_stats, iteration_stats, engine_idx)
-        for logger in self.aggregated_loggers:
-            logger.record(scheduler_stats, iteration_stats, engine_idx)
+
+        self.prometheus_logger.record(scheduler_stats, iteration_stats,
+                                      engine_idx)
 
     def log(self):
         for per_engine_loggers in self.per_engine_logger_dict.values():
             for logger in per_engine_loggers:
                 logger.log()
-        for logger in self.aggregated_loggers:
-            logger.log()
 
     def log_engine_initialized(self):
-        for agg_logger in self.aggregated_loggers:
-            agg_logger.log_engine_initialized()
+        self.prometheus_logger.log_engine_initialized()
+
         for per_engine_loggers in self.per_engine_logger_dict.values():
             for logger in per_engine_loggers:
                 logger.log_engine_initialized()

vllm/v1/worker/gpu_model_runner.py

@@ -42,6 +42,7 @@ 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,
                                                    supports_eagle3,
+                                                   supports_mrope,
                                                    supports_transcription)
 from vllm.model_executor.models.interfaces_base import (
     VllmModelForPooling, is_pooling_model, is_text_generation_model)
@@ -730,16 +731,28 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             if mm_input.get("use_audio_in_video") is True:
                 use_audio_in_video = True
 
-        req_state.mrope_positions, req_state.mrope_position_delta = \
-            MRotaryEmbedding.get_input_positions_tensor(
-                req_state.prompt_token_ids,
-                hf_config=self.model_config.hf_config,
-                image_grid_thw=image_grid_thw,
-                video_grid_thw=video_grid_thw,
-                second_per_grid_ts=second_per_grid_ts,
-                audio_feature_lengths=audio_feature_lengths,
-                use_audio_in_video=use_audio_in_video,
-            )
+        if supports_mrope(self.model):
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                self.model.get_mrope_input_positions(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
+        else:
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                MRotaryEmbedding.get_input_positions_tensor(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
 
     def _extract_mm_kwargs(
         self,

vllm/worker/model_runner.py

@@ -41,7 +41,8 @@ from vllm.model_executor.layers.sampler import (Sampler, SamplerOutput,
                                                 get_sampler)
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
-from vllm.model_executor.models import supports_lora, supports_multimodal
+from vllm.model_executor.models import (supports_lora, supports_mrope,
+                                        supports_multimodal)
 from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs, MultiModalPlaceholderMap,
@@ -670,18 +671,33 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                     inter_data.seq_ids[seq_idx]]
                 token_ids = seq_data.get_token_ids()
 
-                mrope_input_positions, mrope_position_delta = \
-                    MRotaryEmbedding.get_input_positions(
-                        token_ids,
-                        hf_config=hf_config,
-                        image_grid_thw=image_grid_thw,
-                        video_grid_thw=video_grid_thw,
-                        second_per_grid_ts=second_per_grid_ts,
-                        context_len=inter_data.context_lens[seq_idx],
-                        seq_len=inter_data.seq_lens[seq_idx],
-                        audio_feature_lengths=audio_feature_lengths,
-                        use_audio_in_video=use_audio_in_video,
-                    )
+                if supports_mrope(self.runner.model):
+                    mrope_input_positions, mrope_position_delta = \
+                        self.runner.model.get_mrope_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
+                    mrope_input_positions = mrope_input_positions.tolist()
+                else:
+                    mrope_input_positions, mrope_position_delta = \
+                        MRotaryEmbedding.get_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
 
                 seq_data.mrope_position_delta = mrope_position_delta
                 inter_data.mrope_input_positions[