[tab] miles/backends/sglang_utils/sglang_engine.py (python)
import dataclasses
import logging
import multiprocessing
import time

import requests
import sglang_router
from packaging.version import parse
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import kill_process_tree
from urllib3.exceptions import NewConnectionError

from miles.ray.ray_actor import RayActor
from miles.utils.http_utils import get_host_info

logger = logging.getLogger(__name__)


def get_base_gpu_id(args, rank):
    num_gpus = min(args.num_gpus_per_node, args.rollout_num_gpus_per_engine)
    if args.colocate:
        start_index = (rank * num_gpus) % args.num_gpus_per_node
    else:
        num_actor_gpus = 0 if args.debug_rollout_only else args.actor_num_gpus_per_node * args.actor_num_nodes
        start_index = (num_actor_gpus + rank * num_gpus) % args.num_gpus_per_node
        if args.use_critic:
            num_critic_gpus = args.critic_num_gpus_per_node * args.critic_num_nodes
            start_index = (num_actor_gpus + num_critic_gpus + rank * num_gpus) % args.num_gpus_per_node
    return start_index


def launch_server_process(server_args: ServerArgs) -> multiprocessing.Process:
    from sglang.srt.entrypoints.http_server import launch_server

    multiprocessing.set_start_method("spawn", force=True)
    server_args.host = server_args.host.strip("[]")
    p = multiprocessing.Process(target=launch_server, args=(server_args,))
    p.start()

    if server_args.node_rank != 0:
        return

    _wait_server_healthy(
        base_url=server_args.url(),
        api_key=server_args.api_key,
        is_process_alive=lambda: p.is_alive(),
    )

    return p


def _wait_server_healthy(base_url, api_key, is_process_alive):
    headers = {
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": f"Bearer {api_key}",
    }

    with requests.Session() as session:
        while True:
            try:
                response = session.get(f"{base_url}/health_generate", headers=headers)
                if response.status_code == 200:
                    break
            except requests.RequestException:
                pass

            if not is_process_alive():
                raise Exception("Server process terminated unexpectedly.")

            time.sleep(2)

        # use flush_cache to make sure the working queue is empty, so that we can do offload
        while True:
            try:
                response = session.get(f"{base_url}/flush_cache", headers=headers)
                if response.status_code == 200:
                    break

            except requests.RequestException:
                pass

            if not is_process_alive():
                raise Exception("Server process terminated unexpectedly.")

            time.sleep(2)


class SGLangEngine(RayActor):
    def __init__(self, args, rank: int, worker_type: str = "regular"):
        self.args = args
        self.rank = rank
        self.worker_type = worker_type

    def init(self, dist_init_addr, port, nccl_port, host=None):
        self.router_ip = self.args.sglang_router_ip
        self.router_port = self.args.sglang_router_port

        host = host or get_host_info()[1]

        # support ipv6 address
        if ":" in host and not host.startswith("["):
            host = f"[{host}]"

        # dist_init_addr may be 2605:...:10163, should split port
        *addr_parts, port_str = dist_init_addr.split(":")
        ipv6_addr = ":".join(addr_parts)
        if ":" in ipv6_addr and not ipv6_addr.startswith("["):
            dist_init_addr = f"[{ipv6_addr}]:{port_str}"

        server_args_dict, external_engine_need_check_fields = _compute_server_args(
            self.args, self.rank, dist_init_addr, nccl_port, host, port, self.worker_type
        )

        self.node_rank = server_args_dict["node_rank"]
        self.server_host = server_args_dict["host"]
        self.server_port = server_args_dict["port"]

        if self.args.rollout_external:
            self._init_external(server_args_dict, external_engine_need_check_fields=external_engine_need_check_fields)
        else:
            self._init_normal(server_args_dict)

    def _init_external(self, expect_server_args, external_engine_need_check_fields):
        logger.info(f"Use external SGLang engine (rank={self.rank}, expect_server_args={expect_server_args})")

        def _get_actual_server_args():
            response = requests.get(f"http://{self.server_host}:{self.server_port}/get_server_info")
            response.raise_for_status()
            return response.json()

        def _sanity_check_server_args(actual_server_args, expect_server_args):
            for name in external_engine_need_check_fields:
                expect_value = expect_server_args.get(name)
                actual_value = actual_server_args.get(name)
                assert (
                    actual_value == expect_value
                ), f"{name=} {expect_value=} {actual_value=} {expect_server_args=} {actual_server_args=}"

        _wait_server_healthy(
            base_url=f"http://{self.server_host}:{self.server_port}",
            api_key=None,
            is_process_alive=lambda: True,
        )
        actual_server_args = _get_actual_server_args()
        _sanity_check_server_args(actual_server_args, expect_server_args)

    def _init_normal(self, server_args_dict):
        logger.info(f"Launch HttpServerEngineAdapter at: {self.server_host}:{self.server_port}")
        self.process = launch_server_process(ServerArgs(**server_args_dict))

        if self.node_rank == 0 and self.router_ip and self.router_port:
            if parse(sglang_router.__version__) <= parse("0.2.1") or self.args.use_miles_router:
                assert (
                    self.worker_type == "regular"
                ), "pd disaggregation is not supported in old router or miles router."
                response = requests.post(
                    f"http://{self.router_ip}:{self.router_port}/add_worker?url=http://{self.server_host}:{self.server_port}"
                )
            else:
                response = requests.post(
                    f"http://{self.router_ip}:{self.router_port}/workers",
                    json={
                        "url": f"http://{self.server_host}:{self.server_port}",
                        "worker_type": self.worker_type,
                    },
                )
            response.raise_for_status()

    def _make_request(self, endpoint: str, payload: dict | None = None):
        """Make a POST request to the specified endpoint with the given payload.

        Args:
            endpoint: The API endpoint to call
            payload: The JSON payload to send (default: empty dict)

        Returns:
            The JSON response from the server
        """
        if self.node_rank != 0:
            return

        url = f"http://{self.server_host}:{self.server_port}/{endpoint}"
        response = requests.post(url, json=payload or {})
        try:
            response.raise_for_status()
        except requests.exceptions.HTTPError as e:
            e.add_note(f"{response.text=}")
            raise
        return response.json()

    def health_generate(self, timeout: float = 5.0) -> bool:
        """Run /health_generate on the underlying SGLang HTTP server.

        Args:
            timeout: Timeout for the health request in seconds.

        Returns:
            True if the server responds with HTTP 200.

        Raises:
            requests.RequestException: If the request fails for any reason, including timeout.
        """
        if self.node_rank != 0:
            return True

        response = requests.get(
            f"http://{self.server_host}:{self.server_port}/health_generate",
            timeout=timeout,
        )
        response.raise_for_status()
        return True

    def update_weights_from_tensor(
        self,
        serialized_named_tensors: list[str],
        load_format: str | None = None,
        flush_cache: bool = False,
        weight_version: str | None = None,
    ):
        """
        Update model weights from tensor data. The HTTP server will only post meta data, and the real weights will be copied directly from GPUs.

        Note: The model should be on GPUs rather than CPU for this functionality to work properly.
        If you encounter issues, ensure your model is loaded on GPU devices rather than CPU.
        """
        payload = {
            "serialized_named_tensors": serialized_named_tensors,
            "load_format": load_format,
            "flush_cache": flush_cache,
        }
        if weight_version is not None:
            payload["weight_version"] = weight_version
        return self._make_request(
            "update_weights_from_tensor",
            payload,
        )

    def flush_cache(self):
        """Flush the cache of the server."""
        if self.node_rank != 0:
            return
        # flush cache will not return status_code 200 when there are pending requests
        for _ in range(60):
            try:
                response = requests.get(f"http://{self.server_host}:{self.server_port}/flush_cache")
                if response.status_code == 200:
                    break
            except NewConnectionError as e:
                raise e
            except Exception as e:
                logger.info(f"Error flushing cache: {e}")
                time.sleep(1)
                continue
        else:
            raise TimeoutError("Timeout while flushing cache.")

    def shutdown(self):
        if self.args.rollout_external:
            return

        logger.info(f"Shutdown engine {self.server_host}:{self.server_port}...")
        if self.node_rank == 0:
            worker_url = f"http://{self.server_host}:{self.server_port}"
            if parse(sglang_router.__version__) <= parse("0.2.1") or self.args.use_miles_router:
                response = requests.post(
                    f"http://{self.router_ip}:{self.router_port}/remove_worker?url=http://{self.server_host}:{self.server_port}"
                )
            else:
                response = requests.delete(f"http://{self.router_ip}:{self.router_port}/workers/{worker_url}")
            response.raise_for_status()
        kill_process_tree(self.process.pid)

    def get_weight_version(self):
        if self.node_rank != 0:
            return
        url = f"http://{self.server_host}:{self.server_port}/get_weight_version"
        response = requests.get(url)
        response.raise_for_status()
        return response.json()["weight_version"]

    def release_memory_occupation(self):
        self.flush_cache()
        return self._make_request("release_memory_occupation")

    def resume_memory_occupation(self, tags: list[str] = None):
        """
        Available tags for multi-stage resume: weights, kv_cache
        """
        return self._make_request(
            "resume_memory_occupation",
            {"tags": tags},
        )

    def check_weights(self, action: str):
        return self._make_request("check_weights", {"action": action})

    def init_weights_update_group(self, master_address, master_port, rank_offset, world_size, group_name, backend):
        return self._make_request(
            "init_weights_update_group",
            {
                "master_address": master_address,
                "master_port": master_port,
                "rank_offset": rank_offset,
                "world_size": world_size,
                "group_name": group_name,
                "backend": backend,
            },
        )

    def destroy_weights_update_group(self, group_name):
        try:
            return self._make_request(
                "destroy_weights_update_group",
                {
                    "group_name": group_name,
                },
            )
        except requests.exceptions.RequestException:
            # catch the case there the engine is just created and does not have the group.
            pass

    def update_weights_from_distributed(
        self, names, dtypes, shapes, group_name, flush_cache=False, weight_version: str | None = None
    ):
        payload = {
            "names": names,
            "dtypes": [str(dtype).replace("torch.", "") for dtype in dtypes],
            "shapes": shapes,
            "group_name": group_name,
            "flush_cache": flush_cache,
        }
        if weight_version is not None:
            payload["weight_version"] = weight_version
        return self._make_request(
            "update_weights_from_distributed",
            payload,
        )

    def pause_generation(self):
        response = requests.post(f"http://{self.server_host}:{self.server_port}/pause_generation", json={})
        response.raise_for_status()
        return response

    def continue_generation(self):
        response = requests.post(f"http://{self.server_host}:{self.server_port}/continue_generation", json={})
        response.raise_for_status()
        return response

    def start_profile(
        self,
        # The output directory
        output_dir: str | None = None,
        # If set, it profile as many as this number of steps.
        # If it is set, profiling is automatically stopped after this step, and
        # the caller doesn't need to run stop_profile.
        start_step: int | None = None,
        num_steps: int | None = None,
        activities: list[str] | None = None,
        profile_by_stage: bool = False,
        with_stack: bool | None = None,
        record_shapes: bool | None = None,
    ):
        response = requests.post(
            f"http://{self.server_host}:{self.server_port}/start_profile",
            json={
                "output_dir": output_dir,
                "start_step": start_step,
                "num_steps": num_steps,
                "activities": activities,
                "profile_by_stage": profile_by_stage,
                "with_stack": with_stack,
                "record_shapes": record_shapes,
            },
        )
        response.raise_for_status()
        return response

    def stop_profile(self):
        response = requests.post(f"http://{self.server_host}:{self.server_port}/stop_profile", json={})
        response.raise_for_status()
        return response


def _compute_server_args(args, rank, dist_init_addr, nccl_port, host, port, worker_type: str = "regular"):
    nnodes = max(1, args.rollout_num_gpus_per_engine // args.num_gpus_per_node)
    node_rank = rank % nnodes
    kwargs = {
        "model_path": args.hf_checkpoint,
        "trust_remote_code": True,
        "random_seed": args.seed + rank,
        # memory
        "enable_memory_saver": args.offload_rollout,
        # distributed
        "host": host,
        "port": port,
        "nccl_port": nccl_port,
        "nnodes": nnodes,
        "node_rank": node_rank,
        "dist_init_addr": dist_init_addr,
        "gpu_id_step": 1,
        "base_gpu_id": get_base_gpu_id(args, rank),
        # parallel
        "tp_size": args.rollout_num_gpus_per_engine,
        "dp_size": args.sglang_dp_size,
        "pp_size": args.sglang_pp_size,
        "ep_size": args.sglang_ep_size,
        # always skip warmup to prevent warmup timeout.
        "skip_server_warmup": True,
    }

    if worker_type == "prefill":
        kwargs["disaggregation_mode"] = "prefill"
        kwargs["load_balance_method"] = "round_robin"
    elif worker_type == "decode":
        kwargs["disaggregation_mode"] = "decode"
        kwargs["prefill_round_robin_balance"] = True

    if args.use_rollout_routing_replay:
        kwargs["enable_return_routed_experts"] = True
    if args.fp16:
        kwargs["dtype"] = "float16"
    
    # Configure LoRA support if using LoRA
    if args.use_lora:
        kwargs["enable_lora"] = True
        kwargs["max_lora_rank"] = args.lora_rank
        kwargs["lora_target_modules"] = args.lora_target_modules

    external_engine_need_check_fields = [k for k in kwargs.keys() if k not in _EXTERNAL_ENGINE_SKIP_CHECK_FIELDS]

    unused_keys = set(kwargs.keys())
    for attr in dataclasses.fields(ServerArgs):
        if hasattr(args, f"sglang_{attr.name}") and attr.name not in kwargs:
            kwargs[attr.name] = getattr(args, f"sglang_{attr.name}")
        unused_keys.discard(attr.name)

    # for compatibility with old args
    if len(unused_keys) > 0:
        logger.info(f"Warning: The following arguments is not supported in the current sglang: {unused_keys}.")
        for key in unused_keys:
            kwargs.pop(key)

    return kwargs, external_engine_need_check_fields


_EXTERNAL_ENGINE_SKIP_CHECK_FIELDS = [
    "model_path",
    "trust_remote_code",
    "random_seed",
    "nccl_port",
    "dist_init_addr",
    "skip_server_warmup",
]