vllm_serve.py

# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import asyncio
import base64
import logging
import os
from collections.abc import Sequence
from contextlib import asynccontextmanager
from dataclasses import dataclass, field
from io import BytesIO
from itertools import chain
from multiprocessing import Pipe, Process
from multiprocessing.connection import Connection

import torch
import torch.distributed.distributed_c10d as c10d
from packaging.version import Version
from transformers import is_torch_xpu_available, is_vision_available

from trl import TrlParser
from trl.generation.vllm_generation import extract_logprobs
from trl.import_utils import (
    is_fastapi_available,
    is_pydantic_available,
    is_uvicorn_available,
    is_vllm_ascend_available,
    is_vllm_available,
)


if is_fastapi_available():
    from fastapi import FastAPI


if is_pydantic_available():
    from pydantic import BaseModel


if is_uvicorn_available():
    import uvicorn


if is_vision_available():
    from PIL import Image


if is_vllm_available():
    import vllm
    from vllm import LLM, SamplingParams
    from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
    from vllm.distributed.parallel_state import get_world_group
    from vllm.distributed.utils import StatelessProcessGroup

    if Version(vllm.__version__) <= Version("0.11.0"):
        from vllm.utils import get_open_port
    else:
        from vllm.utils.network_utils import get_open_port

    if Version(vllm.__version__) <= Version("0.10.2"):
        from vllm.sampling_params import GuidedDecodingParams
    else:
        from vllm.sampling_params import StructuredOutputsParams

    if is_vllm_ascend_available():
        from vllm_ascend.distributed.device_communicators.pyhccl import PyHcclCommunicator as PyNcclCommunicator


logger = logging.getLogger(__name__)

# We use CUDA with multiprocessing, so we must use the 'spawn' start method. Otherwise, we will get the following
# error: RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use
# the 'spawn' start method
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"


class WeightSyncWorkerExtension:
    """
    A vLLM worker extension that enables weight synchronization between a client and multiple server workers.

    This worker uses a `StatelessProcessGroup` to establish communication and a `PyNcclCommunicator` or
    `ProcessGroupXCCL` to handle efficient GPU-based communication using NCCL. The primary purpose of this class is to
    receive updated model weights from a client process and distribute them to all worker processes participating in
    model inference.
    """

    # The following attributes are initialized when `init_communicator` method is called.
    communicator = None  # Communicator for weight updates
    client_rank = None  # Source rank for broadcasting updated weights

    def init_communicator(self, host: str, port: int, world_size: int, client_device_uuid: str) -> None:
        """
        Initializes the weight update communicator using a stateless process group.

        This method creates a `StatelessProcessGroup` that allows external training processes to communicate with vLLM
        workers without interfering with the global torch distributed group.

        Args:
            host (`str`):
                Hostname or IP address of the master node.
            port (`int`):
                Port number to be used for communication.
            world_size (`int`):
                Total number of participating processes in the update group.
            client_device_uuid (`str`):
                UUID of the device of client main process. Used to assert that devices are different from vllm workers
                devices.
        """
        if self.communicator is not None:
            raise RuntimeError("Weight update group already initialized. Call close_communicator first.")

        # TODO: will remove after torch xpu 2.9 support uuid in get_device_properties
        if torch.cuda.is_available() or (
            is_torch_xpu_available() and hasattr(torch.xpu.get_device_properties(self.device), "uuid")
        ):
            accelerator_module = torch.xpu if is_torch_xpu_available() else torch.cuda
            if client_device_uuid == str(accelerator_module.get_device_properties(self.device).uuid):
                raise RuntimeError(
                    f"Attempting to use the same CUDA device (UUID: {client_device_uuid}) for multiple distinct "
                    "roles/ranks within the same communicator. This setup is unsupported and will likely lead to program "
                    "hangs or incorrect behavior. Ensure that trainer is using different devices than vLLM server."
                )
        # Get the rank of the current worker in the global world group.
        rank = get_world_group().rank

        if is_torch_xpu_available():
            store = torch.distributed.TCPStore(host_name=host, port=port, world_size=world_size, is_master=(rank == 0))
            prefixed_store = c10d.PrefixStore("client2server", store)
            xccl_options = c10d.ProcessGroupXCCL.Options()
            pg = c10d.ProcessGroupXCCL(
                store=prefixed_store,
                rank=rank,
                size=world_size,
                options=xccl_options,
            )
            self.communicator = pg
        else:
            # Create a stateless process group to manage communication between training processes and vLLM workers.
            # Initialize the NCCL-based communicator for weight synchronization.
            pg = StatelessProcessGroup.create(host=host, port=port, rank=rank, world_size=world_size)
            self.communicator = PyNcclCommunicator(pg, device=self.device)

        # The client process that sends updated weights has the highest rank (world_size - 1).
        self.client_rank = world_size - 1

    def update_named_param(self, name: str, dtype: str, shape: Sequence[int]) -> None:
        """
        Receives updated weights from the client process and updates the named parameter in the model.

        Args:
            name (`str`):
                Name of the weight tensor being updated.
            dtype (`str`):
                Data type of the weight tensor as a string (e.g., `"torch.float32"`).
            shape (`Sequence[int]`):
                Shape of the weight tensor.
        """
        if self.communicator is None:
            raise RuntimeError("Communicator not initialized. Call `init_communicator` first.")

        dtype = getattr(torch, dtype.split(".")[-1])
        # Allocate memory for the incoming weight tensor on the correct device.
        weight = torch.empty(shape, dtype=dtype, device=self.device)

        if is_torch_xpu_available():
            # Use XCCL to broadcast the updated weights from the client (src) to all workers.
            self.communicator.broadcast(weight, root=self.client_rank)
            self.communicator.barrier()
        else:
            # Use NCCL to broadcast the updated weights from the client (src) to all workers.
            self.communicator.broadcast(weight, src=self.client_rank)
            self.communicator.group.barrier()

        # Load the received weights into the model.
        self.model_runner.model.load_weights(weights=[(name, weight)])

    def batch_update_named_params(self, params: list[tuple[str, str, tuple[int, ...]]]) -> None:
        """
        Receives and updates multiple named parameters in a single batch.
        This avoids per-parameter HTTP round-trips. The client broadcasts each
        parameter's tensor via NCCL in the same order as the params list.
        Args:
            params: List of (name, dtype, shape) tuples for each parameter.
        """
        if self.communicator is None:
            raise RuntimeError("Communicator not initialized. Call `init_communicator` first.")

        weights_to_load = []
        for name, dtype_str, shape in params:
            dtype = getattr(torch, dtype_str.split(".")[-1])
            weight = torch.empty(shape, dtype=dtype, device=self.device)

            if is_torch_xpu_available():
                self.communicator.broadcast(weight, root=self.client_rank)
            else:
                self.communicator.broadcast(weight, src=self.client_rank)

            weights_to_load.append((name, weight))

        # Single barrier after all broadcasts
        if is_torch_xpu_available():
            self.communicator.barrier()
        else:
            self.communicator.group.barrier()

        # Load all weights at once
        self.model_runner.model.load_weights(weights=weights_to_load)

    def close_communicator(self) -> None:
        """
        Closes the communicator when weight synchronization is no longer needed.

        This method deletes the NCCL communicator to release associated resources.
        """

        if self.communicator is not None:
            del self.communicator
            self.communicator = None  # Ensure attribute is reset to None
            self.client_rank = None  # Ensure attribute is reset to None


@dataclass
class ScriptArguments:
    r"""
    Arguments for the script.

    Args:
        model (`str`):
            Model name or path to load the model from.
        revision (`str`, *optional*):
            Revision to use for the model. If not specified, the default branch will be used.
        tensor_parallel_size (`int`, *optional*, defaults to `1`):
            Number of tensor parallel workers to use.
        data_parallel_size (`int`, *optional*, defaults to `1`):
            Number of data parallel workers to use.
        host (`str`, *optional*, defaults to `"0.0.0.0"`):
            Host address to run the server on.
        port (`int`, *optional*, defaults to `8000`):
            Port to run the server on.
        gpu_memory_utilization (`float`, *optional*, defaults to `0.9`):
            Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache on the
            device dedicated to generation powered by vLLM. Higher values will increase the KV cache size and thus
            improve the model's throughput. However, if the value is too high, it may cause out-of-memory (OOM) errors
            during initialization.
        dtype (`str`, *optional*, defaults to `"auto"`):
            Data type to use for vLLM generation. If set to `"auto"`, the data type will be automatically determined
            based on the model configuration. Find the supported values in the vLLM documentation.
        max_model_len (`int`, *optional*):
            If set, the `max_model_len` to use for vLLM. This can be useful when running with reduced
            `vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model
            context size, which might be much larger than the KV cache, leading to inefficiencies.
        enable_prefix_caching (`bool`, *optional*):
            Whether to enable prefix caching in vLLM. If set to `True`, ensure that the model and the hardware support
            this feature.
        enforce_eager (`bool`, *optional*, defaults to `False`):
            Whether to enforce eager execution. If set to `True`, we will disable CUDA graph and always execute the
            model in eager mode. If `False` (default behavior), we will use CUDA graph and eager execution in hybrid.
        vllm_model_impl (`str`, *optional*, defaults to `"vllm"`):
            Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use
            the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model
            implementation.
        kv_cache_dtype (`str`, *optional*, defaults to `"auto"`):
            Data type to use for KV cache. If set to `"auto"`, the dtype will default to the model data type.
        trust_remote_code (`bool`, *optional*, defaults to `False`):
            Whether to trust remote code when loading models. Set to `True` to allow executing code from model
            repositories. This is required for some custom models but introduces security risks.
        log_level (`str`, *optional*, defaults to `"info"`):
            Log level for uvicorn. Possible choices: `"critical"`, `"error"`, `"warning"`, `"info"`, `"debug"`,
            `"trace"`.
    """

    model: str = field(
        metadata={"help": "Model name or path to load the model from."},
    )
    revision: str | None = field(
        default=None,
        metadata={"help": "Revision to use for the model. If not specified, the default branch will be used."},
    )
    tensor_parallel_size: int = field(
        default=1,
        metadata={"help": "Number of tensor parallel workers to use."},
    )
    data_parallel_size: int = field(
        default=1,
        metadata={"help": "Number of data parallel workers to use."},
    )
    host: str = field(
        default="0.0.0.0",
        metadata={"help": "Host address to run the server on."},
    )
    port: int = field(
        default=8000,
        metadata={"help": "Port to run the server on."},
    )
    gpu_memory_utilization: float = field(
        default=0.9,
        metadata={
            "help": "Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV "
            "cache on the device dedicated to generation powered by vLLM. Higher values will increase the KV cache "
            "size and thus improve the model's throughput. However, if the value is too high, it may cause "
            "out-of-memory (OOM) errors during initialization."
        },
    )
    dtype: str = field(
        default="auto",
        metadata={
            "help": "Data type to use for vLLM generation. If set to 'auto', the data type will be automatically "
            "determined based on the model configuration. Find the supported values in the vLLM documentation."
        },
    )
    max_model_len: int | None = field(
        default=None,
        metadata={
            "help": "If set, the `max_model_len` to use for vLLM. This can be useful when running with reduced "
            "`vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model "
            "context size, which might be much larger than the KV cache, leading to inefficiencies."
        },
    )
    enable_prefix_caching: bool | None = field(
        default=None,
        metadata={
            "help": "Whether to enable prefix caching in vLLM. If set to `True`, ensure that the model and the "
            "hardware support this feature."
        },
    )
    enforce_eager: bool | None = field(
        default=False,
        metadata={
            "help": "Whether to enforce eager execution. If set to `True`, we will disable CUDA graph and always "
            "execute the model in eager mode. If `False` (default behavior), we will use CUDA graph and eager "
            "execution in hybrid."
        },
    )
    kv_cache_dtype: str = field(
        default="auto",
        metadata={
            "help": "Data type to use for KV cache. If set to 'auto', the dtype will default to the model data type."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": "Whether to trust remote code when loading models. Set to True to allow executing code from model "
            "repositories. This is required for some custom models but introduces security risks."
        },
    )
    log_level: str = field(
        default="info",
        metadata={
            "help": "Log level for uvicorn. Possible choices: 'critical', 'error', 'warning', 'info', 'debug', "
            "'trace'."
        },
    )
    vllm_model_impl: str = field(
        default="vllm",
        metadata={
            "help": "Model implementation to use for vLLM. Must be one of `transformers` or `vllm`. `transformers`: "
            "Use the `transformers` backend for model implementation. `vllm`: Use the `vllm` library for "
            "model implementation."
        },
    )


def llm_worker(
    script_args: ScriptArguments, data_parallel_rank: int, master_port: int, connection: Connection
) -> None:
    # Set required environment variables for DP to work with vLLM
    os.environ["VLLM_DP_RANK"] = str(data_parallel_rank)
    os.environ["VLLM_DP_RANK_LOCAL"] = str(data_parallel_rank)
    os.environ["VLLM_DP_SIZE"] = str(script_args.data_parallel_size)
    os.environ["VLLM_DP_MASTER_PORT"] = str(master_port)

    llm = LLM(
        model=script_args.model,
        revision=script_args.revision,
        tensor_parallel_size=script_args.tensor_parallel_size,
        gpu_memory_utilization=script_args.gpu_memory_utilization,
        enforce_eager=script_args.enforce_eager,
        dtype=script_args.dtype,
        # Automatic Prefix Caching caches the KV cache of existing queries, so that a new query can
        # directly reuse the KV cache if it shares the same prefix with one of the existing queries.
        # This is particularly useful here because we generate completions from the same prompts.
        enable_prefix_caching=script_args.enable_prefix_caching,
        kv_cache_dtype=script_args.kv_cache_dtype,
        max_model_len=script_args.max_model_len,
        worker_extension_cls="trl.scripts.vllm_serve.WeightSyncWorkerExtension",
        trust_remote_code=script_args.trust_remote_code,
        model_impl=script_args.vllm_model_impl,
        # Important so temperature scaling/logit tweaking affects the TIS log probs
        logprobs_mode="processed_logprobs",
    )

    # Send ready signal to parent process
    connection.send({"status": "ready"})

    while True:
        # Wait for commands from the parent process
        try:
            command = connection.recv()
        except KeyboardInterrupt:
            llm.collective_rpc(method="close_communicator")
            break

        # Handle commands
        if command["type"] in ["call", "fire_and_forget"]:
            method_name = command["method"]
            args, kwargs = command.get("args", ()), command.get("kwargs", {})
            method = getattr(llm, method_name)
            result = method(*args, **kwargs)
            if command["type"] == "call":
                connection.send(result)
        elif command["type"] == "shutdown":
            break


def chunk_list(lst: list, n: int) -> list[list]:
    """
    Split list `lst` into `n` evenly distributed sublists.

    Example:
    ```python
    >>> chunk_list([1, 2, 3, 4, 5, 6], 2)
    [[1, 2, 3], [4, 5, 6]]

    >>> chunk_list([1, 2, 3, 4, 5, 6], 4)
    [[1, 2], [3, 4], [5], [6]]

    >>> chunk_list([1, 2, 3, 4, 5, 6], 8)
    [[1], [2], [3], [4], [5], [6], [], []]
    ```
    """
    k, r = divmod(len(lst), n)
    return [lst[i * k + min(i, r) : (i + 1) * k + min(i + 1, r)] for i in range(n)]


def main(script_args: ScriptArguments):
    if not is_fastapi_available():
        raise ImportError(
            "FastAPI is required to run the vLLM serve script. Please install it using `pip install fastapi`."
        )

    if not is_pydantic_available():
        raise ImportError(
            "Pydantic is required to run the vLLM serve script. Please install it using `pip install pydantic`."
        )

    if not is_uvicorn_available():
        raise ImportError(
            "Uvicorn is required to run the vLLM serve script. Please install it using `pip install uvicorn`."
        )

    if not is_vllm_available():
        raise ImportError("vLLM is required to run the vLLM serve script. Please install it using `pip install vllm`.")

    # Spawn dp workers, and setup pipes for communication
    master_port = get_open_port()
    connections = []
    processes = []
    for data_parallel_rank in range(script_args.data_parallel_size):
        parent_connection, child_connection = Pipe()
        process = Process(target=llm_worker, args=(script_args, data_parallel_rank, master_port, child_connection))
        process.start()
        connections.append(parent_connection)
        processes.append(process)

    @asynccontextmanager
    async def lifespan(app: FastAPI):
        # Wait for all workers to send "ready"
        ready_connections = set()
        while len(ready_connections) < script_args.data_parallel_size:
            for connection in connections:
                msg = connection.recv()
                if isinstance(msg, dict) and msg.get("status") == "ready":
                    ready_connections.add(connection)

        yield

        # Wait for processes to terminate
        for process in processes:
            process.join(timeout=10)  # Wait for 10 seconds for the process to terminate
            if process.is_alive():
                logger.warning(f"Process {process} is still alive after 10 seconds, attempting to terminate...")
                process.terminate()
                process.join()  # ensure process termination after calling terminate()

    app = FastAPI(lifespan=lifespan)

    # Define the endpoints for the model server
    @app.get("/health/")
    async def health():
        """
        Health check endpoint to verify that the server is running.
        """
        return {"status": "ok"}

    @app.get("/get_world_size/")
    async def get_world_size():
        """
        Retrieves the world size of the LLM engine, which is `tensor_parallel_size * data_parallel_size`.

        Returns:
            `dict`:
                A dictionary containing the world size.

        Example response:
        ```json
        {"world_size": 8}
        ```
        """
        return {"world_size": script_args.tensor_parallel_size * script_args.data_parallel_size}

    class GenerateRequest(BaseModel):
        prompts: list[str]
        images: list[str] | None = None
        n: int = 1
        repetition_penalty: float = 1.0
        temperature: float = 1.0
        top_p: float = 1.0
        top_k: int = -1
        min_p: float = 0.0
        max_tokens: int = 16
        logprobs: int | None = 0
        truncate_prompt_tokens: int | None = None
        structured_outputs_regex: str | None = None
        generation_kwargs: dict = field(default_factory=dict)

    class GenerateResponse(BaseModel):
        prompt_ids: list[list[int]]
        completion_ids: list[list[int]]
        logprobs: list[list[list[float]]]
        logprob_token_ids: list[list[list[int]]]

    @app.post("/generate/", response_model=GenerateResponse)
    async def generate(request: GenerateRequest):
        """
        Generates completions for the provided prompts.

        Args:
            request (`GenerateRequest`):
                - `prompts` (list of `str`): A list of prompts (text strings) for the model to generate completions.
                - `images` (list of `str`, *optional*, default to `None`): A list of base64 encoded images to process
                  along with prompts.
                - `n` (`int`, *optional*, defaults to `1`): Number of completions to generate for each prompt.
                - `repetition_penalty` (`float`, *optional*, defaults to `1.0`): Repetition penalty to apply during
                  generation.
                - `temperature` (`float`, *optional*, defaults to `1.0`): Temperature for sampling. Higher values lead
                  to more random outputs.
                - `top_p` (`float`, *optional*, defaults to `1.0`): Top-p (nucleus) sampling parameter. It controls the
                  diversity of the generated text.
                - `top_k` (`int`, *optional*, defaults to `-1`): Top-k sampling parameter. If set to `-1`, it disables
                  top-k sampling.
                - `min_p` (`float`, *optional*, defaults to `0.0`): Minimum probability threshold for sampling.
                - `max_tokens` (`int`, *optional*, defaults to `16`): Maximum number of tokens to generate for each
                  completion.
                - `logprobs` (`int`, *optional*, defaults to `0`): Number of top logprobs to return per token. When 0,
                  only the sampled token's logprob is returned. When N>0, returns the top-N logprobs sorted by
                  descending probability.
                - `truncate_prompt_tokens` (`int`, *optional*): If set to `-1`, will use the truncation size supported
                  by the model. If set to an integer k, will use only the last k tokens from the prompt (i.e., left
                  truncation). If set to `None`, truncation is disabled.
                - `structured_outputs_regex` (`str`, *optional*): A regex pattern for structured outputs. If provided,
                  the model will only generate tokens that match this regex pattern.
                - `generation_kwargs` (`dict`, *optional*): Additional generation parameters to pass to the vLLM
                  `SamplingParams`. This can include parameters like `seed`, `frequency_penalty`, etc. If it contains
                  keys that conflict with the other parameters, they will override them.

        Returns:
            `GenerateResponse`:
                - `prompt_ids` (list of list of `int`): A list of lists of token IDs for each input prompt.
                - `completion_ids` (list of list of `int`): A list of lists of token IDs for each generated completion.
                - `logprobs` (list of list of list of `float`): Per-token logprobs of shape (num_sequences, seq_len,
                  num_logprobs), sorted by descending probability.
                - `logprob_token_ids` (list of list of list of `int`): Token IDs corresponding to each logprob, same
                  shape as `logprobs`.

        Example request:
        ```json
        {"prompts": ["Hello world", "What is AI?"]}
        ```

        Example response:
        ```json
        {
          "prompt_ids": [[101, 102], [201, 202]],
          "completion_ids": [[103, 104, 105], [203, 204, 205]],
          "logprobs": [[[-0.1], [-0.2], [-0.3]], [[-0.4], [-0.5], [-0.6]]],
          "logprob_token_ids": [[[103], [104], [105]], [[203], [204], [205]]]
        }
        ```
        """
        request.images = request.images or [None] * len(request.prompts)

        prompts = []
        for prompt, image in zip(request.prompts, request.images, strict=True):
            row = {"prompt": prompt}
            if image is not None:
                row["multi_modal_data"] = {"image": Image.open(BytesIO(base64.b64decode(image)))}
            prompts.append(row)

        generation_kwargs = {
            "n": request.n,
            "repetition_penalty": request.repetition_penalty,
            "temperature": request.temperature,
            "top_p": request.top_p,
            "top_k": request.top_k,
            "min_p": request.min_p,
            "max_tokens": request.max_tokens,
            "truncate_prompt_tokens": request.truncate_prompt_tokens,
            "logprobs": request.logprobs,
        }
        generation_kwargs.update(request.generation_kwargs)

        # Structured outputs, if enabled
        if Version(vllm.__version__) <= Version("0.10.2"):
            structured_outputs_key = "guided_decoding"
            if request.structured_outputs_regex is not None:
                if generation_kwargs.get("guided_decoding") is not None:
                    logger.warning(
                        "Both `structured_outputs_regex` and `generation_kwargs['guided_decoding']` are set; "
                        "`structured_outputs_regex` takes precedence."
                    )
                structured_outputs = GuidedDecodingParams(regex=request.structured_outputs_regex)
            else:
                structured_outputs = generation_kwargs.get("guided_decoding")
        else:
            structured_outputs_key = "structured_outputs"
            if request.structured_outputs_regex is not None:
                if generation_kwargs.get("structured_outputs") is not None:
                    logger.warning(
                        "Both `structured_outputs_regex` and `generation_kwargs['structured_outputs']` are set; "
                        "`structured_outputs_regex` takes precedence."
                    )
                structured_outputs = StructuredOutputsParams(regex=request.structured_outputs_regex)
            elif isinstance(generation_kwargs.get("structured_outputs"), dict):
                # If structured_outputs is passed as a dictionary in generation_kwargs, convert it to a
                # StructuredOutputsParams object to ensure compatibility with vLLM's SamplingParams.
                structured_outputs_dict = generation_kwargs.get("structured_outputs")
                structured_outputs = StructuredOutputsParams(**structured_outputs_dict)
            else:
                structured_outputs = generation_kwargs.get("structured_outputs")

        generation_kwargs[structured_outputs_key] = structured_outputs
        sampling_params = SamplingParams(**generation_kwargs)

        # Evenly distribute prompts across DP ranks
        chunked_prompts = chunk_list(prompts, script_args.data_parallel_size)

        # Send the prompts to each worker
        for connection, prompts in zip(connections, chunked_prompts, strict=True):
            # When the number of prompts is less than data_parallel_size, some workers will receive empty prompts.
            # However, vLLM requires that we always send at least one prompt. So we send a placeholder prompt to comply
            # with vLLM's requirement, and we later ignore the result.
            if not prompts:
                prompts = ["<placeholder>"]
            kwargs = {"prompts": prompts, "sampling_params": sampling_params}
            connection.send({"type": "call", "method": "generate", "kwargs": kwargs})

        # Receive results
        all_outputs = [connection.recv() for connection in connections]

        # Handle empty prompts (see above)
        all_outputs = [output for output, prompts in zip(all_outputs, chunked_prompts, strict=True) if prompts]

        # Flatten and combine all results
        all_outputs = list(chain.from_iterable(all_outputs))  # from list of list to single list
        prompt_ids = [output.prompt_token_ids for output in all_outputs]
        completion_ids = [list(output.token_ids) for outputs in all_outputs for output in outputs.outputs]
        logprobs, logprob_token_ids = extract_logprobs(all_outputs)

        return {
            "prompt_ids": prompt_ids,
            "completion_ids": completion_ids,
            "logprobs": logprobs,
            "logprob_token_ids": logprob_token_ids,
        }

    class ChatRequest(BaseModel):
        messages: list[list[dict]]
        n: int = 1
        repetition_penalty: float = 1.0
        temperature: float = 1.0
        top_p: float = 1.0
        top_k: int = -1
        min_p: float = 0.0
        max_tokens: int = 16
        logprobs: int | None = 0
        truncate_prompt_tokens: int | None = None
        structured_outputs_regex: str | None = None
        generation_kwargs: dict = field(default_factory=dict)
        chat_template_kwargs: dict = field(default_factory=dict)

    class ChatResponse(BaseModel):
        prompt_ids: list[list[int]]
        completion_ids: list[list[int]]
        logprobs: list[list[list[float]]]
        logprob_token_ids: list[list[list[int]]]

    @app.post("/chat/", response_model=ChatResponse)
    async def chat(request: ChatRequest):
        """
        Generates completions for the provided chat messages.

        Args:
            request (`ChatRequest`):
                - `messages` (list of `dict`): A list of messages (dicts with "role" and "content" keys) for the model
                  to generate completions.
                - `n` (`int`, *optional*, defaults to `1`): Number of completions to generate for each prompt.
                - `repetition_penalty` (`float`, *optional*, defaults to `1.0`): Repetition penalty to apply during
                  generation.
                - `temperature` (`float`, *optional*, defaults to `1.0`): Temperature for sampling. Higher values lead
                  to more random outputs.
                - `top_p` (`float`, *optional*, defaults to `1.0`): Top-p (nucleus) sampling parameter. It controls the
                  diversity of the generated text.
                - `top_k` (`int`, *optional*, defaults to `-1`): Top-k sampling parameter. If set to `-1`, it disables
                  top-k sampling.
                - `min_p` (`float`, *optional*, defaults to `0.0`): Minimum probability threshold for sampling.
                - `max_tokens` (`int`, *optional*, defaults to `16`): Maximum number of tokens to generate for each
                  completion.
                - `logprobs` (`int`, *optional*, defaults to `0`): Number of top logprobs to return per token. When 0,
                  only the sampled token's logprob is returned. When N>0, returns the top-N logprobs sorted by
                  descending probability.
                - `truncate_prompt_tokens` (`int`, *optional*): If set to `-1`, will use the truncation size supported
                  by the model. If set to an integer k, will use only the last k tokens from the prompt (i.e., left
                  truncation). If set to `None`, truncation is disabled.
                - `structured_outputs_regex` (`str`, *optional*): A regex pattern for structured outputs. If provided,
                  the model will only generate tokens that match this regex pattern.
                - `generation_kwargs` (`dict`, *optional*): Additional generation parameters to pass to the vLLM
                  `SamplingParams`. This can include parameters like `seed`, `frequency_penalty`, etc. If it contains
                  keys that conflict with the other parameters, they will override them.
                - `chat_template_kwargs` (`dict`, *optional*): Additional keyword arguments to pass to the chat
                  template.

        Returns:
            `ChatResponse`:
                - `prompt_ids` (list of list of `int`): A list of lists of token IDs for each input prompt.
                - `completion_ids` (list of list of `int`): A list of lists of token IDs for each generated completion.
                - `logprobs` (list of list of list of `float`): Per-token logprobs of shape (num_sequences, seq_len,
                  num_logprobs), sorted by descending probability.
                - `logprob_token_ids` (list of list of list of `int`): Token IDs corresponding to each logprob, same
                  shape as `logprobs`.

        Example request:
        ```bash
        curl -X POST 'http://0.0.0.0:8000/chat/' \
          -H 'Content-Type: application/json' \
          -d '{"messages": [[{ "role": "user", "content": "Hello!" }]]}'
        ```

        Example response:
        ```json
        {
            "prompt_ids": [[151644, 872, 198, 9707, 0, 151645, 198, 151644, 77091, 198]],
            "completion_ids": [[151667, 198, 32313, 11, 279]],
            "logprobs": [[[-0.0003], [-3.58e-07], [-0.0902], [-6.39e-05], [-0.0387]]],
            "logprob_token_ids": [[[151667], [198], [32313], [11], [279]]]
        }
        ```
        """
        # Convert PIL images to base64 strings
        for message_list in request.messages:
            for message in message_list:
                if isinstance(message["content"], list):
                    for part in message["content"]:
                        if part["type"] == "image_pil":
                            part["image_pil"] = Image.open(BytesIO(base64.b64decode(part["image_pil"])))

        generation_kwargs = {
            "n": request.n,
            "repetition_penalty": request.repetition_penalty,
            "temperature": request.temperature,
            "top_p": request.top_p,
            "top_k": request.top_k,
            "min_p": request.min_p,
            "max_tokens": request.max_tokens,
            "truncate_prompt_tokens": request.truncate_prompt_tokens,
            "logprobs": request.logprobs,
        }
        generation_kwargs.update(request.generation_kwargs)

        # Structured outputs, if enabled
        if Version(vllm.__version__) <= Version("0.10.2"):
            structured_outputs_key = "guided_decoding"
            if request.structured_outputs_regex is not None:
                if generation_kwargs.get("guided_decoding") is not None:
                    logger.warning(
                        "Both `structured_outputs_regex` and `generation_kwargs['guided_decoding']` are set; "
                        "`structured_outputs_regex` takes precedence."
                    )
                structured_outputs = GuidedDecodingParams(regex=request.structured_outputs_regex)
            else:
                structured_outputs = generation_kwargs.get("guided_decoding")
        else:
            structured_outputs_key = "structured_outputs"
            if request.structured_outputs_regex is not None:
                if generation_kwargs.get("structured_outputs") is not None:
                    logger.warning(
                        "Both `structured_outputs_regex` and `generation_kwargs['structured_outputs']` are set; "
                        "`structured_outputs_regex` takes precedence."
                    )
                structured_outputs = StructuredOutputsParams(regex=request.structured_outputs_regex)
            elif isinstance(generation_kwargs.get("structured_outputs"), dict):
                # If structured_outputs is passed as a dictionary in generation_kwargs, convert it to a
                # StructuredOutputsParams object to ensure compatibility with vLLM's SamplingParams.
                structured_outputs_dict = generation_kwargs.get("structured_outputs")
                structured_outputs = StructuredOutputsParams(**structured_outputs_dict)
            else:
                structured_outputs = generation_kwargs.get("structured_outputs")

        generation_kwargs[structured_outputs_key] = structured_outputs
        sampling_params = SamplingParams(**generation_kwargs)

        # Evenly distribute prompts across DP ranks
        chunked_messages = chunk_list(request.messages, script_args.data_parallel_size)

        # Send the messages to each worker
        for connection, messages in zip(connections, chunked_messages, strict=True):
            # When the number of messages is less than data_parallel_size, some workers will receive empty messages.
            # However, vLLM requires that we always send at least one prompt. So we send a placeholder prompt to comply
            # with vLLM's requirement, and we later ignore the result.
            if not messages:
                messages = [[{"role": "user", "content": "<placeholder>"}]]
            kwargs = {
                "messages": messages,
                "sampling_params": sampling_params,
                "chat_template_kwargs": request.chat_template_kwargs,
            }
            connection.send({"type": "call", "method": "chat", "kwargs": kwargs})

        # Receive results
        all_outputs = [connection.recv() for connection in connections]

        # Handle empty prompts (see above)
        all_outputs = [output for output, prompts in zip(all_outputs, chunked_messages, strict=True) if prompts]

        # Flatten and combine all results
        all_outputs = list(chain.from_iterable(all_outputs))  # from list of list to single list
        prompt_ids = [output.prompt_token_ids for output in all_outputs]
        completion_ids = [list(output.token_ids) for outputs in all_outputs for output in outputs.outputs]
        logprobs, logprob_token_ids = extract_logprobs(all_outputs)

        return {
            "prompt_ids": prompt_ids,
            "completion_ids": completion_ids,
            "logprobs": logprobs,
            "logprob_token_ids": logprob_token_ids,
        }

    class InitCommunicatorRequest(BaseModel):
        host: str
        port: int
        world_size: int
        client_device_uuid: str

    @app.post("/init_communicator/")
    async def init_communicator(request: InitCommunicatorRequest):
        """
        Initializes the communicator for synchronizing model weights between a client and multiple server workers.

        Args:
            request (`InitCommunicatorRequest`):
                - `host` (`str`): Hostname or IP address of the master node.
                - `port` (`int`): Port number to be used for communication.
                - `world_size` (`int`): Total number of participating processes in the group.
                - `client_device_uuid` (`str`): UUID of the device of client main process. Used to assert that devices
                  are different from vLLM workers devices.
        """
        world_size = script_args.tensor_parallel_size * script_args.data_parallel_size + 1

        # The function init_communicator is called this way: init_communicator(host, port, world_size)
        # So with collective_rpc we need to call it this way:
        # llm.collective_rpc(method="init_communicator", args=(host, port, world_size))
        kwargs = {
            "method": "init_communicator",
            "args": (request.host, request.port, world_size, request.client_device_uuid),
        }
        msg = {"type": "fire_and_forget", "method": "collective_rpc", "kwargs": kwargs}
        loop = asyncio.get_running_loop()
        await asyncio.gather(*(loop.run_in_executor(None, conn.send, msg) for conn in connections))

        return {"message": "Request received, initializing communicator"}

    class UpdateWeightsRequest(BaseModel):
        name: str
        dtype: str
        shape: list[int]

    @app.post("/update_named_param/")
    async def update_named_param(request: UpdateWeightsRequest):
        """
        Updates the model weights with the provided tensor.

        Once this endpoint is called, the client process should broadcast the updated weights to all server workers.

        Args:
            request (`UpdateWeightsRequest`):
                - `name` (`str`): Name of the weight tensor being updated.
                - `dtype` (`str`): Data type of the weight tensor (e.g., `"torch.float32"`).
                - `shape` (list of `int`): Shape of the weight

        """
        # The function update_named_param is called this way: update_named_param("name", "torch.float32", (10, 10))
        # So with collective_rpc we need to call it this way:
        # llm.collective_rpc("update_named_param", args=("name", "torch.float32", (10, 10)))
        kwargs = {"method": "update_named_param", "args": (request.name, request.dtype, tuple(request.shape))}
        msg = {"type": "fire_and_forget", "method": "collective_rpc", "kwargs": kwargs}
        loop = asyncio.get_running_loop()
        await asyncio.gather(*(loop.run_in_executor(None, conn.send, msg) for conn in connections))

        return {"message": "Request received, updating named parameter"}

    class BatchUpdateWeightsRequest(BaseModel):
        params: list[dict]  # List of {"name": str, "dtype": str, "shape": list[int]}

    @app.post("/batch_update_named_params/")
    async def batch_update_named_params(request: BatchUpdateWeightsRequest):
        """Batch update: sends all param metadata in one HTTP call, then NCCL broadcasts happen in sequence."""

        params_list = [(p["name"], p["dtype"], tuple(p["shape"])) for p in request.params]
        kwargs = {"method": "batch_update_named_params", "args": (params_list,)}
        msg = {"type": "fire_and_forget", "method": "collective_rpc", "kwargs": kwargs}
        loop = asyncio.get_running_loop()
        await asyncio.gather(*(loop.run_in_executor(None, conn.send, msg) for conn in connections))
        return {"message": f"Batch update started for {len(params_list)} parameters"}

    @app.post("/reset_prefix_cache/")
    async def reset_prefix_cache():
        """
        Resets the prefix cache for the model.
        """
        for connection in connections:
            connection.send({"type": "call", "method": "reset_prefix_cache"})
        # Wait for and collect all results
        all_outputs = [connection.recv() for connection in connections]
        success = all(output for output in all_outputs)
        return {"message": "Request received, resetting prefix cache status: " + str(success)}

    @app.post("/close_communicator/")
    async def close_communicator():
        """
        Closes the weight update group and cleans up associated resources.
        """
        kwargs = {"method": "close_communicator"}
        for connection in connections:
            connection.send({"type": "fire_and_forget", "method": "collective_rpc", "kwargs": kwargs})
        return {"message": "Request received, closing communicator"}

    # Start the server
    uvicorn.run(app, host=script_args.host, port=script_args.port, log_level=script_args.log_level)


def make_parser(subparsers: argparse._SubParsersAction | None = None):
    if subparsers is not None:
        parser = subparsers.add_parser("vllm-serve", help="Run the vLLM serve script", dataclass_types=ScriptArguments)
    else:
        parser = TrlParser(ScriptArguments)
    return parser


if __name__ == "__main__":
    parser = make_parser()
    (script_args,) = parser.parse_args_and_config()
    main(script_args)