tokenizer_manager.py

# Copyright 2023-2024 SGLang Team
# 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.
# ==============================================================================
"""TokenizerManager is a process that tokenizes the text."""

import asyncio
import copy
import dataclasses
import json
import logging
import math
import os
import pickle
import signal
import sys
import threading
import time
import uuid
from collections import deque
from contextlib import nullcontext
from datetime import datetime
from http import HTTPStatus
from typing import (
    Any,
    Awaitable,
    Deque,
    Dict,
    Generic,
    List,
    Optional,
    Tuple,
    TypeVar,
    Union,
)

import fastapi
import torch
import uvloop
import zmq
import zmq.asyncio
from fastapi import BackgroundTasks

from sglang.srt.aio_rwlock import RWLock
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.disaggregation.utils import (
    DisaggregationMode,
    KVClassType,
    TransferBackend,
    get_kv_class,
)
from sglang.srt.hf_transformers_utils import (
    get_processor,
    get_tokenizer,
    get_tokenizer_from_processor,
)
from sglang.srt.lora.lora_registry import LoRARef, LoRARegistry
from sglang.srt.managers.io_struct import (
    AbortReq,
    BatchEmbeddingOut,
    BatchMultimodalOut,
    BatchStrOut,
    BatchTokenIDOut,
    CloseSessionReqInput,
    ConfigureLoggingReq,
    EmbeddingReqInput,
    ExpertDistributionReq,
    ExpertDistributionReqOutput,
    FlushCacheReqInput,
    FlushCacheReqOutput,
    GenerateReqInput,
    GetInternalStateReq,
    GetInternalStateReqOutput,
    GetWeightsByNameReqInput,
    GetWeightsByNameReqOutput,
    HealthCheckOutput,
    InitWeightsUpdateGroupReqInput,
    InitWeightsUpdateGroupReqOutput,
    LoadLoRAAdapterReqInput,
    LoadLoRAAdapterReqOutput,
    LoRAUpdateResult,
    OpenSessionReqInput,
    OpenSessionReqOutput,
    ProfileReq,
    ProfileReqOutput,
    ProfileReqType,
    ReleaseMemoryOccupationReqInput,
    ReleaseMemoryOccupationReqOutput,
    ResumeMemoryOccupationReqInput,
    ResumeMemoryOccupationReqOutput,
    SessionParams,
    SetInternalStateReq,
    SetInternalStateReqOutput,
    SlowDownReqInput,
    SlowDownReqOutput,
    TokenizedEmbeddingReqInput,
    TokenizedGenerateReqInput,
    UnloadLoRAAdapterReqInput,
    UnloadLoRAAdapterReqOutput,
    UpdateWeightFromDiskReqInput,
    UpdateWeightFromDiskReqOutput,
    UpdateWeightsFromDistributedReqInput,
    UpdateWeightsFromDistributedReqOutput,
    UpdateWeightsFromTensorReqInput,
    UpdateWeightsFromTensorReqOutput,
)
from sglang.srt.managers.mm_utils import TensorTransportMode
from sglang.srt.managers.multimodal_processor import get_mm_processor, import_processors
from sglang.srt.managers.scheduler_input_blocker import input_blocker_guard_region
from sglang.srt.metrics.collector import TokenizerMetricsCollector
from sglang.srt.sampling.sampling_params import SamplingParams
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    dataclass_to_string_truncated,
    get_bool_env_var,
    get_zmq_socket,
    kill_process_tree,
)
from sglang.utils import TypeBasedDispatcher, get_exception_traceback

asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())

logger = logging.getLogger(__name__)


@dataclasses.dataclass
class ReqState:
    """Store the state a request."""

    out_list: List[Dict[Any, Any]]
    finished: bool
    event: asyncio.Event
    obj: Union[GenerateReqInput, EmbeddingReqInput]

    # For metrics
    created_time: float
    finished_time: float = 0.0
    first_token_time: float = 0.0
    last_time: float = 0.0
    last_completion_tokens: int = 1

    # For streaming output
    last_output_offset: int = 0

    # For incremental state update.
    # TODO(lianmin): do not initialize some lists if not needed.
    text: str = ""
    output_ids: List[int] = dataclasses.field(default_factory=list)
    input_token_logprobs_val: List[float] = dataclasses.field(default_factory=list)
    input_token_logprobs_idx: List[int] = dataclasses.field(default_factory=list)
    output_token_logprobs_val: List[float] = dataclasses.field(default_factory=list)
    output_token_logprobs_idx: List[int] = dataclasses.field(default_factory=list)
    input_top_logprobs_val: List[List[float]] = dataclasses.field(default_factory=list)
    input_top_logprobs_idx: List[List[int]] = dataclasses.field(default_factory=list)
    output_top_logprobs_val: List[List[float]] = dataclasses.field(default_factory=list)
    output_top_logprobs_idx: List[List[int]] = dataclasses.field(default_factory=list)
    input_token_ids_logprobs_val: List = dataclasses.field(default_factory=list)
    input_token_ids_logprobs_idx: List = dataclasses.field(default_factory=list)
    output_token_ids_logprobs_val: List = dataclasses.field(default_factory=list)
    output_token_ids_logprobs_idx: List = dataclasses.field(default_factory=list)


class TokenizerManager:
    """TokenizerManager is a process that tokenizes the text."""

    def __init__(
        self,
        server_args: ServerArgs,
        port_args: PortArgs,
    ):
        # Parse args
        self.server_args = server_args
        self.enable_metrics = server_args.enable_metrics
        self.log_requests = server_args.log_requests
        self.log_requests_level = server_args.log_requests_level
        self.preferred_sampling_params = (
            json.loads(server_args.preferred_sampling_params)
            if server_args.preferred_sampling_params
            else None
        )
        self.crash_dump_folder = server_args.crash_dump_folder

        # Read model args
        self.model_path = server_args.model_path
        self.served_model_name = server_args.served_model_name
        self.model_config = ModelConfig.from_server_args(server_args)
        self.is_generation = self.model_config.is_generation
        self.is_image_gen = self.model_config.is_image_gen
        self.context_len = self.model_config.context_len
        self.image_token_id = self.model_config.image_token_id
        self.max_req_input_len = None  # Will be set later in engine.py

        if self.model_config.is_multimodal:
            import_processors()
            try:
                _processor = get_processor(
                    server_args.tokenizer_path,
                    tokenizer_mode=server_args.tokenizer_mode,
                    trust_remote_code=server_args.trust_remote_code,
                    revision=server_args.revision,
                    use_fast=not server_args.disable_fast_image_processor,
                )
            except ValueError as e:
                error_message = str(e)
                if "does not have a slow version" in error_message:
                    logger.info(
                        f"Processor {server_args.tokenizer_path} does not have a slow version. Automatically use fast version"
                    )
                    _processor = get_processor(
                        server_args.tokenizer_path,
                        tokenizer_mode=server_args.tokenizer_mode,
                        trust_remote_code=server_args.trust_remote_code,
                        revision=server_args.revision,
                        use_fast=True,
                    )
                else:
                    raise e
            transport_mode = _determine_tensor_transport_mode(self.server_args)

            # We want to parallelize the image pre-processing so we create an executor for it
            # We create mm_processor for any skip_tokenizer_init to make sure we still encode
            # images even with skip_tokenizer_init=False.
            self.mm_processor = get_mm_processor(
                self.model_config.hf_config, server_args, _processor, transport_mode
            )

            if server_args.skip_tokenizer_init:
                self.tokenizer = self.processor = None
            else:
                self.processor = _processor
                self.tokenizer = get_tokenizer_from_processor(self.processor)
                os.environ["TOKENIZERS_PARALLELISM"] = "false"
        else:
            self.mm_processor = None

            if server_args.skip_tokenizer_init:
                self.tokenizer = self.processor = None
            else:
                self.tokenizer = get_tokenizer(
                    server_args.tokenizer_path,
                    tokenizer_mode=server_args.tokenizer_mode,
                    trust_remote_code=server_args.trust_remote_code,
                    revision=server_args.revision,
                )

        # Init inter-process communication
        context = zmq.asyncio.Context(2)
        self.recv_from_detokenizer = get_zmq_socket(
            context, zmq.PULL, port_args.tokenizer_ipc_name, True
        )
        self.send_to_scheduler = get_zmq_socket(
            context, zmq.PUSH, port_args.scheduler_input_ipc_name, True
        )

        # Request states
        self.no_create_loop = False
        self.rid_to_state: Dict[str, ReqState] = {}
        self.asyncio_tasks = set()

        # Health check
        self.health_check_failed = False
        self.gracefully_exit = False
        self.last_receive_tstamp = 0

        # Dumping
        self.dump_requests_folder = ""  # By default do not dump
        self.dump_requests_threshold = 1000
        self.dump_request_list: List[Tuple] = []
        self.log_request_metadata = self.get_log_request_metadata()
        self.crash_dump_request_list: deque[Tuple] = deque()
        self.crash_dump_performed = False  # Flag to ensure dump is only called once

        # Session
        self.session_futures = {}  # session_id -> asyncio event

        # Weight updates
        # The event to notify the weight sync is finished.
        self.model_update_lock = RWLock()
        self.model_update_result: Optional[Awaitable[UpdateWeightFromDiskReqOutput]] = (
            None
        )
        self._is_updating = False
        self._is_updating_cond = asyncio.Condition()

        # LoRA
        # Initialize the `LoRARegistry` with initial LoRA adapter paths provided in `server_args`.
        # The registry dynamically updates as adapters are loaded / unloaded during runtime. It
        # serves as the source of truth for available adapters and maps user-friendly LoRA names
        # to internally used unique LoRA IDs.
        self.lora_registry = LoRARegistry(self.server_args.lora_paths or {})
        # Lock to serialize LoRA update operations.
        # Please note that, unlike `model_update_lock`, this does not block inference, allowing
        # LoRA updates and inference to overlap.
        self.lora_update_lock = asyncio.Lock()

        # For PD disaggregtion
        self.disaggregation_mode = DisaggregationMode(
            self.server_args.disaggregation_mode
        )
        self.disaggregation_transfer_backend = TransferBackend(
            self.server_args.disaggregation_transfer_backend
        )
        # Start kv boostrap server on prefill
        if self.disaggregation_mode == DisaggregationMode.PREFILL:
            # only start bootstrap server on prefill tm
            kv_bootstrap_server_class = get_kv_class(
                self.disaggregation_transfer_backend, KVClassType.BOOTSTRAP_SERVER
            )
            self.bootstrap_server = kv_bootstrap_server_class(
                self.server_args.disaggregation_bootstrap_port
            )
            is_create_store = (
                self.server_args.node_rank == 0
                and self.server_args.disaggregation_transfer_backend == "ascend"
            )
            if is_create_store:
                try:
                    from mf_adapter import create_config_store

                    ascend_url = os.getenv("ASCEND_MF_STORE_URL")
                    create_config_store(ascend_url)
                except Exception as e:
                    error_message = f"Failed create mf store, invalid ascend_url."
                    error_message += f" With exception {e}"
                    raise error_message

        # For load balancing
        self.current_load = 0
        self.current_load_lock = asyncio.Lock()

        # Metrics
        if self.enable_metrics:
            self.metrics_collector = TokenizerMetricsCollector(
                labels={
                    "model_name": self.server_args.served_model_name,
                    # TODO: Add lora name/path in the future,
                },
                bucket_time_to_first_token=self.server_args.bucket_time_to_first_token,
                bucket_e2e_request_latency=self.server_args.bucket_e2e_request_latency,
                bucket_inter_token_latency=self.server_args.bucket_inter_token_latency,
                collect_tokens_histogram=self.server_args.collect_tokens_histogram,
            )

        # Communicators
        self.init_weights_update_group_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.update_weights_from_distributed_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.update_weights_from_tensor_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.get_weights_by_name_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.release_memory_occupation_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.resume_memory_occupation_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.slow_down_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.flush_cache_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.profile_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.get_internal_state_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.set_internal_state_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.expert_distribution_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )
        self.update_lora_adapter_communicator = _Communicator(
            self.send_to_scheduler, server_args.dp_size
        )

        self._result_dispatcher = TypeBasedDispatcher(
            [
                (
                    (
                        BatchStrOut,
                        BatchEmbeddingOut,
                        BatchTokenIDOut,
                        BatchMultimodalOut,
                    ),
                    self._handle_batch_output,
                ),
                (AbortReq, self._handle_abort_req),
                (OpenSessionReqOutput, self._handle_open_session_req_output),
                (
                    UpdateWeightFromDiskReqOutput,
                    self._handle_update_weights_from_disk_req_output,
                ),
                (
                    InitWeightsUpdateGroupReqOutput,
                    self.init_weights_update_group_communicator.handle_recv,
                ),
                (
                    UpdateWeightsFromDistributedReqOutput,
                    self.update_weights_from_distributed_communicator.handle_recv,
                ),
                (
                    UpdateWeightsFromTensorReqOutput,
                    self.update_weights_from_tensor_communicator.handle_recv,
                ),
                (
                    GetWeightsByNameReqOutput,
                    self.get_weights_by_name_communicator.handle_recv,
                ),
                (
                    ReleaseMemoryOccupationReqOutput,
                    self.release_memory_occupation_communicator.handle_recv,
                ),
                (
                    ResumeMemoryOccupationReqOutput,
                    self.resume_memory_occupation_communicator.handle_recv,
                ),
                (
                    SlowDownReqOutput,
                    self.slow_down_communicator.handle_recv,
                ),
                (
                    FlushCacheReqOutput,
                    self.flush_cache_communicator.handle_recv,
                ),
                (
                    ProfileReqOutput,
                    self.profile_communicator.handle_recv,
                ),
                (
                    GetInternalStateReqOutput,
                    self.get_internal_state_communicator.handle_recv,
                ),
                (
                    SetInternalStateReqOutput,
                    self.set_internal_state_communicator.handle_recv,
                ),
                (
                    ExpertDistributionReqOutput,
                    self.expert_distribution_communicator.handle_recv,
                ),
                (
                    LoRAUpdateResult,
                    self.update_lora_adapter_communicator.handle_recv,
                ),
                (HealthCheckOutput, lambda x: None),
            ]
        )

    async def generate_request(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
        request: Optional[fastapi.Request] = None,
    ):
        created_time = time.time()
        self.auto_create_handle_loop()
        obj.normalize_batch_and_arguments()

        async with self._is_updating_cond:
            await self._is_updating_cond.wait_for(lambda: not self._is_updating)

        if self.log_requests:
            max_length, skip_names, _ = self.log_request_metadata
            logger.info(
                f"Receive: obj={dataclass_to_string_truncated(obj, max_length, skip_names=skip_names)}"
            )

        async with self.model_update_lock.reader_lock:
            if obj.is_single:
                tokenized_obj = await self._tokenize_one_request(obj)
                state = self._send_one_request(obj, tokenized_obj, created_time)
                async for response in self._wait_one_response(obj, state, request):
                    yield response
            else:
                async for response in self._handle_batch_request(
                    obj, request, created_time
                ):
                    yield response

    async def _tokenize_one_request(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
    ):
        """Tokenize one request."""
        # Tokenize
        input_embeds = None
        input_text = obj.text
        token_type_ids = None
        is_cross_encoder_request = (
            isinstance(obj, EmbeddingReqInput) and obj.is_cross_encoder_request
        )
        if obj.input_embeds is not None:
            if not self.server_args.disable_radix_cache:
                raise ValueError(
                    "input_embeds is provided while disable_radix_cache is False. "
                    "Please add `--disable-radix-cache` when you launch the server "
                    "if you want to use input_embeds as inputs."
                )
            input_embeds = obj.input_embeds
            input_ids = obj.input_ids
        elif obj.input_ids is not None:
            input_ids = obj.input_ids
        else:
            if self.tokenizer is None:
                raise ValueError(
                    "The engine initialized with skip_tokenizer_init=True cannot "
                    "accept text prompts. Please provide input_ids or re-initialize "
                    "the engine with skip_tokenizer_init=False."
                )
            encoded = self.tokenizer(
                input_text, return_token_type_ids=is_cross_encoder_request
            )

            input_ids = encoded["input_ids"]
            if is_cross_encoder_request:
                input_ids = encoded["input_ids"][0]
                token_type_ids = encoded.get("token_type_ids", [None])[0]

        if self.mm_processor and obj.contains_mm_input():
            if not isinstance(obj.image_data, list):
                obj.image_data = [obj.image_data]
            if not isinstance(obj.audio_data, list):
                obj.audio_data = [obj.audio_data]
            mm_inputs: Dict = await self.mm_processor.process_mm_data_async(
                image_data=obj.image_data,
                audio_data=obj.audio_data,
                input_text=input_text or input_ids,
                request_obj=obj,
                max_req_input_len=self.max_req_input_len,
            )
            if mm_inputs and "input_ids" in mm_inputs:
                input_ids = mm_inputs["input_ids"]
        else:
            mm_inputs = None

        if self.server_args.enable_lora and obj.lora_path:
            # Start tracking ongoing requests for LoRA adapters and replace the user-friendly LoRA names in
            # `lora_path` with their corresponding unique LoRA IDs, as required for internal processing.
            obj.lora_path = await self.lora_registry.acquire(obj.lora_path)

        self._validate_one_request(obj, input_ids)
        return self._create_tokenized_object(
            obj, input_text, input_ids, input_embeds, mm_inputs, token_type_ids
        )

    def _validate_one_request(
        self, obj: Union[GenerateReqInput, EmbeddingReqInput], input_ids: List[int]
    ) -> None:
        """Validates that the input token count and the requested token count doesn't exceed the model's context length."""

        input_token_num = len(input_ids) if input_ids is not None else 0
        # Check if input alone exceeds context length
        if input_token_num >= self.context_len:
            raise ValueError(
                f"The input ({input_token_num} tokens) is longer than the "
                f"model's context length ({self.context_len} tokens)."
            )

        if isinstance(obj, EmbeddingReqInput) and self.is_generation:
            raise ValueError(
                "This model does not appear to be an embedding model by default. "
                "Please add `--is-embedding` when launching the server or try another model."
            )

        # Check total tokens (input + max_new_tokens)
        max_new_tokens = obj.sampling_params.get("max_new_tokens")
        if (
            max_new_tokens is not None
            and (max_new_tokens + input_token_num) >= self.context_len
        ):
            total_tokens = max_new_tokens + input_token_num
            error_msg = (
                f"Requested token count exceeds the model's maximum context length "
                f"of {self.context_len} tokens. You requested a total of {total_tokens} "
                f"tokens: {input_token_num} tokens from the input messages and "
                f"{max_new_tokens} tokens for the completion. Please reduce the number "
                f"of tokens in the input messages or the completion to fit within the limit."
            )
            raise ValueError(error_msg)

        if isinstance(obj, GenerateReqInput):
            if (
                obj.return_hidden_states
                and not self.server_args.enable_return_hidden_states
            ):
                raise ValueError(
                    "The server is not configured to return the hidden states. "
                    "Please set `--enable-return-hidden-states` to enable this feature."
                )
            if (
                obj.custom_logit_processor
                and not self.server_args.enable_custom_logit_processor
            ):
                raise ValueError(
                    "The server is not configured to enable custom logit processor. "
                    "Please set `--enable-custom-logits-processor` to enable this feature."
                )

    def _validate_input_ids_in_vocab(
        self, input_ids: List[int], vocab_size: int
    ) -> None:
        if any(id >= vocab_size for id in input_ids):
            raise ValueError(
                f"The input_ids {input_ids} contains values greater than the vocab size ({vocab_size})."
            )

    def _create_tokenized_object(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
        input_text: str,
        input_ids: List[int],
        input_embeds: Optional[Union[List[float], None]] = None,
        mm_inputs: Optional[Dict] = None,
        token_type_ids: Optional[List[int]] = None,
    ) -> Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput]:
        """Create a tokenized request object from common parameters."""
        # Parse sampling parameters
        # Note: if there are preferred sampling params, we use them if they are not
        # explicitly passed in sampling_params
        if self.preferred_sampling_params:
            sampling_kwargs = {**self.preferred_sampling_params, **obj.sampling_params}
        else:
            sampling_kwargs = obj.sampling_params
        sampling_params = SamplingParams(**sampling_kwargs)
        sampling_params.normalize(self.tokenizer)
        sampling_params.verify(self.model_config.vocab_size)

        # Build return object
        if isinstance(obj, GenerateReqInput):
            session_params = (
                SessionParams(**obj.session_params) if obj.session_params else None
            )

            tokenized_obj = TokenizedGenerateReqInput(
                obj.rid,
                input_text,
                input_ids,
                mm_inputs,
                sampling_params,
                obj.return_logprob,
                obj.logprob_start_len,
                obj.top_logprobs_num,
                obj.token_ids_logprob,
                obj.stream,
                bootstrap_host=obj.bootstrap_host,
                bootstrap_port=obj.bootstrap_port,
                bootstrap_room=obj.bootstrap_room,
                lora_path=obj.lora_path,
                input_embeds=input_embeds,
                session_params=session_params,
                custom_logit_processor=obj.custom_logit_processor,
                return_hidden_states=obj.return_hidden_states,
                data_parallel_rank=obj.data_parallel_rank,
            )
        elif isinstance(obj, EmbeddingReqInput):
            tokenized_obj = TokenizedEmbeddingReqInput(
                obj.rid,
                input_text,
                input_ids,
                mm_inputs,
                token_type_ids,
                sampling_params,
            )

        return tokenized_obj

    async def _batch_tokenize_and_process(
        self, batch_size: int, obj: Union[GenerateReqInput, EmbeddingReqInput]
    ) -> List[Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput]]:
        """Handle batch tokenization for text inputs only."""
        logger.debug(f"Starting batch tokenization for {batch_size} text requests")

        # Collect requests and texts
        requests = [obj[i] for i in range(batch_size)]
        texts = [req.text for req in requests]

        # Batch tokenize all texts
        encoded = self.tokenizer(texts)
        input_ids_list = encoded["input_ids"]

        # Process all requests
        tokenized_objs = []
        for i, req in enumerate(requests):
            self._validate_token_len(obj[i], input_ids_list[i])
            tokenized_objs.append(
                self._create_tokenized_object(
                    req, req.text, input_ids_list[i], None, None
                )
            )
        logger.debug(f"Completed batch processing for {batch_size} requests")
        return tokenized_objs

    def _validate_batch_tokenization_constraints(
        self, batch_size: int, obj: Union[GenerateReqInput, EmbeddingReqInput]
    ) -> None:
        """Validate constraints for batch tokenization processing."""
        for i in range(batch_size):
            if self.is_generation and obj[i].contains_mm_input():
                raise ValueError(
                    "For multimodal input processing do not set `enable_tokenizer_batch_encode`."
                )
            if obj[i].input_ids is not None:
                raise ValueError(
                    "Batch tokenization is not needed for pre-tokenized input_ids. Do not set `enable_tokenizer_batch_encode`."
                )
            if obj[i].input_embeds is not None:
                raise ValueError(
                    "Batch tokenization is not needed for input_embeds. Do not set `enable_tokenizer_batch_encode`."
                )

    def _send_one_request(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
        tokenized_obj: Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput],
        created_time: Optional[float] = None,
    ):
        self.send_to_scheduler.send_pyobj(tokenized_obj)
        state = ReqState([], False, asyncio.Event(), obj, created_time=created_time)
        self.rid_to_state[obj.rid] = state
        return state

    async def _wait_one_response(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
        state: ReqState,
        request: Optional[fastapi.Request] = None,
    ):
        """Wait for the response of one request."""
        while True:
            try:
                await asyncio.wait_for(state.event.wait(), timeout=4)
            except asyncio.TimeoutError:
                if request is not None and await request.is_disconnected():
                    # Abort the request for disconnected requests (non-streaming, waiting queue)
                    self.abort_request(obj.rid)
                    # Use exception to kill the whole call stack and asyncio task
                    raise ValueError(
                        f"Request is disconnected from the client side (type 1). Abort request {obj.rid=}"
                    )
                continue

            out = state.out_list[-1]

            state.out_list = []
            if state.finished:
                if self.log_requests:
                    max_length, skip_names, out_skip_names = self.log_request_metadata
                    if self.model_config.is_multimodal_gen:
                        msg = f"Finish: obj={dataclass_to_string_truncated(obj, max_length, skip_names=skip_names)}"
                    else:
                        msg = f"Finish: obj={dataclass_to_string_truncated(obj, max_length, skip_names=skip_names)}, out={dataclass_to_string_truncated(out, max_length, skip_names=out_skip_names)}"
                    logger.info(msg)

                # Mark ongoing LoRA request as finished.
                if self.server_args.enable_lora and obj.lora_path:
                    await self.lora_registry.release(obj.lora_path)

                # Check if this was an abort/error created by scheduler
                if isinstance(out["meta_info"].get("finish_reason"), dict):
                    finish_reason = out["meta_info"]["finish_reason"]
                    if (
                        finish_reason.get("type") == "abort"
                        and finish_reason.get("status_code") == HTTPStatus.BAD_REQUEST
                    ):
                        raise ValueError(finish_reason["message"])

                    if (
                        finish_reason.get("type") == "abort"
                        and finish_reason.get("status_code")
                        == HTTPStatus.SERVICE_UNAVAILABLE
                    ):
                        # This is an abort request initiated by scheduler.
                        # Delete the key to prevent resending abort request to the scheduler and
                        # to ensure aborted request state is cleaned up.
                        del self.rid_to_state[state.obj.rid]
                        raise fastapi.HTTPException(
                            status_code=finish_reason["status_code"],
                            detail=finish_reason["message"],
                        )
                yield out
                break

            state.event.clear()

            if obj.stream:
                yield out
            else:
                if request is not None and await request.is_disconnected():
                    # Abort the request for disconnected requests (non-streaming, running)
                    self.abort_request(obj.rid)
                    # Use exception to kill the whole call stack and asyncio task
                    raise ValueError(
                        f"Request is disconnected from the client side (type 3). Abort request {obj.rid=}"
                    )

    async def _handle_batch_request(
        self,
        obj: Union[GenerateReqInput, EmbeddingReqInput],
        request: Optional[fastapi.Request] = None,
        created_time: Optional[float] = None,
    ):
        batch_size = obj.batch_size

        generators = []
        rids = []
        if getattr(obj, "parallel_sample_num", 1) == 1:
            if self.server_args.enable_tokenizer_batch_encode:
                # Validate batch tokenization constraints
                self._validate_batch_tokenization_constraints(batch_size, obj)

                tokenized_objs = await self._batch_tokenize_and_process(batch_size, obj)

                for i, tokenized_obj in enumerate(tokenized_objs):
                    tmp_obj = obj[i]
                    state = self._send_one_request(tmp_obj, tokenized_obj, created_time)
                    generators.append(self._wait_one_response(tmp_obj, state, request))
                    rids.append(tmp_obj.rid)
            else:
                # Sequential tokenization and processing
                with (
                    input_blocker_guard_region(send_to_scheduler=self.send_to_scheduler)
                    if get_bool_env_var("SGLANG_ENABLE_COLOCATED_BATCH_GEN")
                    else nullcontext()
                ):
                    for i in range(batch_size):
                        tmp_obj = obj[i]
                        tokenized_obj = await self._tokenize_one_request(tmp_obj)
                        state = self._send_one_request(
                            tmp_obj, tokenized_obj, created_time
                        )
                        generators.append(
                            self._wait_one_response(tmp_obj, state, request)
                        )
                        rids.append(tmp_obj.rid)
        else:
            # FIXME: When using batch and parallel_sample_num together, the perf is not optimal.
            if batch_size > 128:
                logger.warning(
                    "Sending a single large batch with parallel sampling (n > 1) has not been well optimized. "
                    "The performance might be better if you just duplicate the requests n times or use "
                    "many threads to send them one by one with parallel sampling (n > 1)."
                )

            # Tokenize all requests
            objs = [obj[i] for i in range(batch_size)]
            tokenized_objs = await asyncio.gather(
                *(self._tokenize_one_request(obj) for obj in objs)
            )

            # Cache the common prefix for parallel sampling
            for i in range(batch_size):
                tmp_obj = copy.copy(objs[i])
                tokenized_obj = copy.copy(tokenized_objs[i])
                tokenized_obj.rid = tmp_obj.regenerate_rid()
                tokenized_obj.sampling_params = copy.copy(tokenized_obj.sampling_params)
                tokenized_obj.sampling_params.max_new_tokens = 0
                tokenized_obj.stream = False
                state = self._send_one_request(tmp_obj, tokenized_obj, created_time)
                await self._wait_one_response(tmp_obj, state, request).__anext__()

            # Expand requests, assign new rids for them, and send them
            for i in range(batch_size):
                for _ in range(obj.parallel_sample_num):
                    tmp_obj = copy.copy(objs[i])
                    tokenized_obj = copy.copy(tokenized_objs[i])
                    tokenized_obj.rid = tmp_obj.regenerate_rid()
                    state = self._send_one_request(tmp_obj, tokenized_obj, created_time)
                    generators.append(self._wait_one_response(tmp_obj, state, request))
                    rids.append(tmp_obj.rid)

        # Wait for all requests
        is_stream = hasattr(obj, "stream") and obj.stream
        if not is_stream:
            outputs = await asyncio.gather(*(gen.__anext__() for gen in generators))
            yield outputs
        else:
            rid_to_index = {rid: i for i, rid in enumerate(rids)}
            task_map = {asyncio.create_task(gen.__anext__()): gen for gen in generators}
            while task_map:
                done, _ = await asyncio.wait(
                    task_map.keys(), return_when=asyncio.FIRST_COMPLETED
                )

                for task in done:
                    gen = task_map.pop(task)
                    try:
                        result = task.result()
                        result["index"] = rid_to_index[result["meta_info"]["id"]]
                        yield result
                        new_task = asyncio.create_task(gen.__anext__())
                        task_map[new_task] = gen
                    except StopAsyncIteration:
                        pass

    async def flush_cache(self) -> FlushCacheReqOutput:
        return (await self.flush_cache_communicator(FlushCacheReqInput()))[0]

    def abort_request(self, rid: str = "", abort_all: bool = False):
        if not abort_all and rid not in self.rid_to_state:
            return
        req = AbortReq(rid, abort_all)
        self.send_to_scheduler.send_pyobj(req)

        if self.enable_metrics:
            self.metrics_collector.observe_one_aborted_request()

    async def start_profile(
        self,
        output_dir: Optional[str] = None,
        start_step: Optional[int] = None,
        num_steps: Optional[int] = None,
        activities: Optional[List[str]] = None,
        with_stack: Optional[bool] = None,
        record_shapes: Optional[bool] = None,
        profile_by_stage: bool = False,
    ):
        self.auto_create_handle_loop()
        env_with_stack: bool = get_bool_env_var("SGLANG_PROFILE_WITH_STACK", "true")
        with_stack = False if with_stack is False or env_with_stack is False else True
        req = ProfileReq(
            type=ProfileReqType.START_PROFILE,
            output_dir=output_dir,
            start_step=start_step,
            num_steps=num_steps,
            activities=activities,
            with_stack=with_stack,
            record_shapes=record_shapes,
            profile_by_stage=profile_by_stage,
            profile_id=str(time.time()),
        )
        return await self._execute_profile(req)

    async def stop_profile(self):
        self.auto_create_handle_loop()
        req = ProfileReq(type=ProfileReqType.STOP_PROFILE)
        return await self._execute_profile(req)

    async def _execute_profile(self, req: ProfileReq):
        result = (await self.profile_communicator(req))[0]
        if not result.success:
            raise RuntimeError(result.message)
        return result

    async def start_expert_distribution_record(self):
        self.auto_create_handle_loop()
        await self.expert_distribution_communicator(ExpertDistributionReq.START_RECORD)

    async def stop_expert_distribution_record(self):
        self.auto_create_handle_loop()
        await self.expert_distribution_communicator(ExpertDistributionReq.STOP_RECORD)

    async def dump_expert_distribution_record(self):
        self.auto_create_handle_loop()
        await self.expert_distribution_communicator(ExpertDistributionReq.DUMP_RECORD)

    async def pause_generation(self):
        async with self._is_updating_cond:
            self._is_updating = True
            self.abort_request(abort_all=True)

    async def continue_generation(self):
        async with self._is_updating_cond:
            self._is_updating = False
            self._is_updating_cond.notify_all()

    async def update_weights_from_disk(
        self,
        obj: UpdateWeightFromDiskReqInput,
        request: Optional[fastapi.Request] = None,
    ) -> Tuple[bool, str]:
        self.auto_create_handle_loop()

        # default the load format to the server_args
        if obj.load_format is None:
            obj.load_format = self.server_args.load_format
        logger.info("Start update_weights. Load format=%s", obj.load_format)

        if obj.abort_all_requests:
            self.abort_request(abort_all=True)

        if True:  # Keep this redundant check to simplify some internal code sync
            # Hold the lock if it is not async. This means that weight sync
            # cannot run while requests are in progress.
            async with self.model_update_lock.writer_lock:
                return await self._wait_for_model_update_from_disk(obj)

    async def _wait_for_model_update_from_disk(
        self, obj: UpdateWeightFromDiskReqInput
    ) -> Tuple[bool, str]:
        self.send_to_scheduler.send_pyobj(obj)
        self.model_update_result = asyncio.Future()
        if self.server_args.dp_size == 1:
            result = await self.model_update_result
            if result.success:
                self.served_model_name = obj.model_path
                self.server_args.model_path = obj.model_path
                self.server_args.load_format = obj.load_format
                self.model_path = obj.model_path
            return result.success, result.message, result.num_paused_requests
        else:  # self.server_args.dp_size > 1
            self.model_update_tmp = []
            result = await self.model_update_result

            all_success = all([r.success for r in result])
            if all_success is True:
                self.server_args.model_path = obj.model_path
                self.server_args.load_format = obj.load_format
                self.model_path = obj.model_path
            all_message = [r.message for r in result]
            all_message = " | ".join(all_message)
            all_paused_requests = [r.num_paused_requests for r in result]
            return all_success, all_message, all_paused_requests

    async def init_weights_update_group(
        self,
        obj: InitWeightsUpdateGroupReqInput,
        request: Optional[fastapi.Request] = None,
    ) -> Tuple[bool, str]:
        self.auto_create_handle_loop()
        assert (
            self.server_args.dp_size == 1
        ), "dp_size must be 1 for init parameter update group"
        result = (await self.init_weights_update_group_communicator(obj))[0]
        return result.success, result.message

    async def update_weights_from_distributed(
        self,
        obj: UpdateWeightsFromDistributedReqInput,
        request: Optional[fastapi.Request] = None,
    ) -> Tuple[bool, str]:
        self.auto_create_handle_loop()
        assert (
            self.server_args.dp_size == 1 or self.server_args.enable_dp_attention
        ), "dp_size must be 1 or dp attention must be enabled for update weights from distributed"

        if obj.abort_all_requests:
            self.abort_request(abort_all=True)

        # This means that weight sync
        # cannot run while requests are in progress.
        async with self.model_update_lock.writer_lock:
            result = (await self.update_weights_from_distributed_communicator(obj))[0]
            return result.success, result.message

    async def update_weights_from_tensor(
        self,
        obj: UpdateWeightsFromTensorReqInput,
        request: Optional[fastapi.Request] = None,
    ) -> Tuple[bool, str]:
        self.auto_create_handle_loop()
        assert (
            self.server_args.dp_size == 1 or self.server_args.enable_dp_attention
        ), "dp_size must be 1 or dp attention must be enabled for update weights from tensor"

        if obj.abort_all_requests:
            self.abort_request(abort_all=True)

        # This means that weight sync
        # cannot run while requests are in progress.
        async with self.model_update_lock.writer_lock:
            result = (await self.update_weights_from_tensor_communicator(obj))[0]
            return result.success, result.message

    async def load_lora_adapter(
        self,
        obj: LoadLoRAAdapterReqInput,
        _: Optional[fastapi.Request] = None,
    ) -> LoadLoRAAdapterReqOutput:
        self.auto_create_handle_loop()
        if not self.server_args.enable_lora:
            raise ValueError(
                "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
            )

        # TODO (lifuhuang): Remove this after we verify that dynamic lora loading works
        # with dp_size > 1.
        assert (
            self.server_args.dp_size == 1
        ), "dp_size must be 1 for dynamic lora loading"
        logger.info(
            "Start load Lora adapter. Lora name=%s, path=%s",
            obj.lora_name,
            obj.lora_path,
        )

        async with self.lora_update_lock:
            # Generate new uniquely identifiable LoRARef object.
            new_adapter = LoRARef(
                lora_name=obj.lora_name,
                lora_path=obj.lora_path,
            )

            # Trigger the actual loading operation at the backend processes.
            obj.lora_id = new_adapter.lora_id
            result = (await self.update_lora_adapter_communicator(obj))[0]

            # Register the LoRA adapter only after loading is successful.
            if result.success:
                await self.lora_registry.register(new_adapter)

            return result

    async def unload_lora_adapter(
        self,
        obj: UnloadLoRAAdapterReqInput,
        _: Optional[fastapi.Request] = None,
    ) -> UnloadLoRAAdapterReqOutput:
        self.auto_create_handle_loop()
        if not self.server_args.enable_lora:
            raise ValueError(
                "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
            )

        assert (
            obj.lora_name is not None
        ), "lora_name must be provided to unload LoRA adapter"

        # TODO (lifuhuang): Remove this after we verify that dynamic lora loading works
        # with dp_size > 1.
        assert (
            self.server_args.dp_size == 1
        ), "dp_size must be 1 for dynamic lora loading"
        logger.info(
            "Start unload Lora adapter. Lora name=%s",
            obj.lora_name,
        )

        async with self.lora_update_lock:
            # Unregister the LoRA adapter from the registry to stop new requests for this adapter
            # from being started.
            lora_id = await self.lora_registry.unregister(obj.lora_name)
            obj.lora_id = lora_id

            # Initiate the actual unloading operation at the backend processes only after all
            # ongoing requests using this LoRA adapter are finished.
            await self.lora_registry.wait_for_unload(lora_id)
            result = (await self.update_lora_adapter_communicator(obj))[0]

            return result

    async def get_weights_by_name(
        self, obj: GetWeightsByNameReqInput, request: Optional[fastapi.Request] = None
    ):
        self.auto_create_handle_loop()
        results = await self.get_weights_by_name_communicator(obj)
        all_parameters = [r.parameter for r in results]
        if self.server_args.dp_size == 1:
            return all_parameters[0]
        else:
            return all_parameters

    async def release_memory_occupation(
        self,
        obj: ReleaseMemoryOccupationReqInput,
        request: Optional[fastapi.Request] = None,
    ):
        self.auto_create_handle_loop()
        await self.release_memory_occupation_communicator(obj)

    async def resume_memory_occupation(
        self,
        obj: ResumeMemoryOccupationReqInput,
        request: Optional[fastapi.Request] = None,
    ):
        self.auto_create_handle_loop()
        await self.resume_memory_occupation_communicator(obj)

    async def slow_down(
        self,
        obj: SlowDownReqInput,
        request: Optional[fastapi.Request] = None,
    ):
        self.auto_create_handle_loop()
        await self.slow_down_communicator(obj)

    async def open_session(
        self, obj: OpenSessionReqInput, request: Optional[fastapi.Request] = None
    ):
        self.auto_create_handle_loop()

        if obj.session_id is None:
            obj.session_id = uuid.uuid4().hex
        elif obj.session_id in self.session_futures:
            return None

        self.send_to_scheduler.send_pyobj(obj)

        self.session_futures[obj.session_id] = asyncio.Future()
        session_id = await self.session_futures[obj.session_id]
        del self.session_futures[obj.session_id]
        return session_id

    async def close_session(
        self, obj: CloseSessionReqInput, request: Optional[fastapi.Request] = None
    ):
        await self.send_to_scheduler.send_pyobj(obj)

    async def get_internal_state(self) -> List[Dict[Any, Any]]:
        req = GetInternalStateReq()
        responses: List[GetInternalStateReqOutput] = (
            await self.get_internal_state_communicator(req)
        )
        # Many DP ranks
        return [res.internal_state for res in responses]

    async def set_internal_state(
        self, obj: SetInternalStateReq
    ) -> SetInternalStateReqOutput:
        responses: List[SetInternalStateReqOutput] = (
            await self.set_internal_state_communicator(obj)
        )
        return [res.internal_state for res in responses]

    async def get_load(self) -> dict:
        # TODO(lsyin): fake load report server
        if not self.current_load_lock.locked():
            async with self.current_load_lock:
                internal_state = await self.get_internal_state()
                self.current_load = internal_state[0]["load"]
        return {"load": self.current_load}

    def get_log_request_metadata(self):
        max_length = None
        skip_names = None
        out_skip_names = None
        if self.log_requests:
            if self.log_requests_level == 0:
                max_length = 1 << 30
                skip_names = set(
                    [
                        "text",
                        "input_ids",
                        "input_embeds",
                        "image_data",
                        "audio_data",
                        "lora_path",
                        "sampling_params",
                    ]
                )
                out_skip_names = set(
                    [
                        "text",
                        "output_ids",
                        "embedding",
                    ]
                )
            elif self.log_requests_level == 1:
                max_length = 1 << 30
                skip_names = set(
                    [
                        "text",
                        "input_ids",
                        "input_embeds",
                        "image_data",
                        "audio_data",
                        "lora_path",
                    ]
                )
                out_skip_names = set(
                    [
                        "text",
                        "output_ids",
                        "embedding",
                    ]
                )
            elif self.log_requests_level == 2:
                max_length = 2048
            elif self.log_requests_level == 3:
                max_length = 1 << 30
            else:
                raise ValueError(
                    f"Invalid --log-requests-level: {self.log_requests_level=}"
                )
        return max_length, skip_names, out_skip_names

    def configure_logging(self, obj: ConfigureLoggingReq):
        if obj.log_requests is not None:
            self.log_requests = obj.log_requests
        if obj.log_requests_level is not None:
            self.log_requests_level = obj.log_requests_level
        if obj.dump_requests_folder is not None:
            self.dump_requests_folder = obj.dump_requests_folder
        if obj.dump_requests_threshold is not None:
            self.dump_requests_threshold = obj.dump_requests_threshold
        if obj.crash_dump_folder is not None:
            self.crash_dump_folder = obj.crash_dump_folder
        logging.info(f"Config logging: {obj=}")
        self.log_request_metadata = self.get_log_request_metadata()

    def create_abort_task(self, obj: GenerateReqInput):
        # Abort the request if the client is disconnected.
        async def abort_request():
            await asyncio.sleep(2)
            if obj.is_single:
                self.abort_request(obj.rid)
            else:
                for rid in obj.rid:
                    self.abort_request(rid)

        background_tasks = BackgroundTasks()
        background_tasks.add_task(abort_request)
        return background_tasks

    def auto_create_handle_loop(self):
        if self.no_create_loop:
            return

        self.no_create_loop = True
        loop = asyncio.get_event_loop()
        self.asyncio_tasks.add(
            loop.create_task(print_exception_wrapper(self.handle_loop))
        )

        self.event_loop = loop

        # We cannot add signal handler when the tokenizer manager is not in
        # the main thread due to the CPython limitation.
        if threading.current_thread() is threading.main_thread():
            signal_handler = SignalHandler(self)
            loop.add_signal_handler(signal.SIGTERM, signal_handler.sigterm_handler)
            # Update the signal handler for the process. It overrides the sigquit handler in the launch phase.
            loop.add_signal_handler(
                signal.SIGQUIT, signal_handler.running_phase_sigquit_handler
            )
        else:
            logger.warning(
                "Signal handler is not added because the tokenizer manager is "
                "not in the main thread. This disables graceful shutdown of the "
                "tokenizer manager when SIGTERM is received."
            )
        self.asyncio_tasks.add(
            loop.create_task(print_exception_wrapper(self.sigterm_watchdog))
        )

    def dump_requests_before_crash(self):
        if self.crash_dump_performed:
            logger.info(
                "SIGTERM/SIGQUIT/Exception triggered, but crash dump already performed, skipping."
            )
            return

        if not self.crash_dump_folder:
            return

        logger.error(f"Dumping requests before crash. {self.crash_dump_folder=}")
        self.crash_dump_performed = True

        # Check if NFS directory is available
        # expected_nfs_dir = "/" + self.crash_dump_folder.lstrip("/").split("/")[0]
        # use_nfs_dir = os.path.isdir(expected_nfs_dir) and os.access(
        #     expected_nfs_dir, os.W_OK
        # )
        use_nfs_dir = False
        if not use_nfs_dir:
            logger.error(
                f"Expected NFS directory is not available or writable. Uploading to GCS."
            )

        data_to_dump = []
        if self.crash_dump_request_list:
            data_to_dump.extend(self.crash_dump_request_list)

        # Add unfinished requests from rid_to_state
        unfinished_requests = []
        for rid, state in self.rid_to_state.items():
            if not state.finished:
                unfinished_requests.append(
                    (
                        state.obj,
                        state.out_list[-1] if state.out_list else {},
                        state.created_time,
                        time.time(),
                    )
                )
        if unfinished_requests:
            data_to_dump.extend(unfinished_requests)

        if not data_to_dump:
            return

        object_name = f'crash_dump_{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.pkl'
        filename = os.path.join(
            self.crash_dump_folder,
            os.getenv("HOSTNAME", None),
            object_name,
        )

        os.makedirs(os.path.dirname(filename), exist_ok=True)
        # Include server_args in the dump
        data_to_dump_with_server_args = {
            "server_args": self.server_args,
            "requests": data_to_dump,
        }
        with open(filename, "wb") as f:
            pickle.dump(data_to_dump_with_server_args, f)
        logger.error(
            f"Dumped {len(self.crash_dump_request_list)} finished and {len(unfinished_requests)} unfinished requests before crash to {filename}"
        )

        def _upload_file_to_gcs(bucket_name, source_file_path, object_name):
            from google.cloud import storage

            client = storage.Client()
            bucket = client.bucket(bucket_name)
            blob = bucket.blob(object_name)
            blob.upload_from_filename(source_file_path, if_generation_match=0)
            logger.error(
                f"Successfully uploaded {source_file_path} to gs://{bucket_name}/{object_name}"
            )

        if not use_nfs_dir:
            _upload_file_to_gcs(
                "sglang_crash_dump",
                filename,
                os.getenv("HOSTNAME", None) + "/" + object_name,
            )

    async def sigterm_watchdog(self):
        while not self.gracefully_exit:
            await asyncio.sleep(5)

        # Drain requests
        while True:
            remain_num_req = len(self.rid_to_state)

            if self.health_check_failed:
                # if health check failed, we should exit immediately
                logger.error(
                    "Signal SIGTERM received while health check failed. Exiting... remaining number of requests: %d",
                    remain_num_req,
                )
                self.dump_requests_before_crash()
                break

            elif get_bool_env_var("SGL_FORCE_SHUTDOWN"):
                # if force shutdown flag set, exit immediately
                logger.error(
                    "Signal SIGTERM received while force shutdown flag set. Force exiting... remaining number of requests: %d",
                    remain_num_req,
                )
                break

            logger.info(
                f"Gracefully exiting... remaining number of requests {remain_num_req}"
            )
            if remain_num_req > 0:
                await asyncio.sleep(5)
            else:
                self.dump_requests_before_crash()
                break

        kill_process_tree(os.getpid(), include_parent=True)
        sys.exit(0)

    async def handle_loop(self):
        """The event loop that handles requests"""

        while True:
            recv_obj = await self.recv_from_detokenizer.recv_pyobj()
            self._result_dispatcher(recv_obj)
            self.last_receive_tstamp = time.time()

    def _handle_batch_output(
        self,
        recv_obj: Union[
            BatchStrOut, BatchEmbeddingOut, BatchMultimodalOut, BatchTokenIDOut
        ],
    ):
        for i, rid in enumerate(recv_obj.rids):
            state = self.rid_to_state.get(rid, None)
            if state is None:
                logger.error(
                    f"Received output for {rid=} but the state was deleted in TokenizerManager."
                )
                continue

            # Build meta_info and return value
            meta_info = {
                "id": rid,
                "finish_reason": recv_obj.finished_reasons[i],
                "prompt_tokens": recv_obj.prompt_tokens[i],
            }

            if getattr(state.obj, "return_logprob", False):
                self.convert_logprob_style(
                    meta_info,
                    state,
                    state.obj.top_logprobs_num,
                    state.obj.token_ids_logprob,
                    state.obj.return_text_in_logprobs
                    and not self.server_args.skip_tokenizer_init,
                    recv_obj,
                    i,
                )

            if not isinstance(recv_obj, BatchEmbeddingOut):
                meta_info.update(
                    {
                        "completion_tokens": recv_obj.completion_tokens[i],
                        "cached_tokens": recv_obj.cached_tokens[i],
                    }
                )

            if getattr(recv_obj, "output_hidden_states", None):
                meta_info["hidden_states"] = recv_obj.output_hidden_states[i]

            if isinstance(recv_obj, BatchStrOut):
                state.text += recv_obj.output_strs[i]
                out_dict = {
                    "text": state.text,
                    "meta_info": meta_info,
                }
            elif isinstance(recv_obj, BatchTokenIDOut):
                if self.server_args.stream_output and state.obj.stream:
                    state.output_ids.extend(recv_obj.output_ids[i])
                    output_token_ids = state.output_ids[state.last_output_offset :]
                    state.last_output_offset = len(state.output_ids)
                else:
                    state.output_ids.extend(recv_obj.output_ids[i])
                    output_token_ids = state.output_ids.copy()

                out_dict = {
                    "output_ids": output_token_ids,
                    "meta_info": meta_info,
                }
            elif isinstance(recv_obj, BatchMultimodalOut):
                raise NotImplementedError("BatchMultimodalOut not implemented")
            else:
                assert isinstance(recv_obj, BatchEmbeddingOut)
                out_dict = {
                    "embedding": recv_obj.embeddings[i],
                    "meta_info": meta_info,
                }

            state.finished = recv_obj.finished_reasons[i] is not None
            if state.finished:
                if self.server_args.speculative_algorithm:
                    meta_info["spec_verify_ct"] = recv_obj.spec_verify_ct[i]
                state.finished_time = time.time()
                meta_info["e2e_latency"] = state.finished_time - state.created_time
                del self.rid_to_state[rid]

            state.out_list.append(out_dict)
            state.event.set()

            # Log metrics and dump
            if self.enable_metrics and state.obj.log_metrics:
                self.collect_metrics(state, recv_obj, i)
            if self.dump_requests_folder and state.finished and state.obj.log_metrics:
                self.dump_requests(state, out_dict)
            if self.crash_dump_folder and state.finished and state.obj.log_metrics:
                self.record_request_for_crash_dump(state, out_dict)

    def convert_logprob_style(
        self,
        meta_info: dict,
        state: ReqState,
        top_logprobs_num: int,
        token_ids_logprob: List[int],
        return_text_in_logprobs: bool,
        recv_obj: BatchStrOut,
        recv_obj_index: int,
    ):
        if recv_obj.input_token_logprobs_val is None:
            return

        if len(recv_obj.input_token_logprobs_val) > 0:
            state.input_token_logprobs_val.extend(
                recv_obj.input_token_logprobs_val[recv_obj_index]
            )
            state.input_token_logprobs_idx.extend(
                recv_obj.input_token_logprobs_idx[recv_obj_index]
            )
        state.output_token_logprobs_val.extend(
            recv_obj.output_token_logprobs_val[recv_obj_index]
        )
        state.output_token_logprobs_idx.extend(
            recv_obj.output_token_logprobs_idx[recv_obj_index]
        )
        meta_info["input_token_logprobs"] = self.detokenize_logprob_tokens(
            state.input_token_logprobs_val,
            state.input_token_logprobs_idx,
            return_text_in_logprobs,
        )
        meta_info["output_token_logprobs"] = self.detokenize_logprob_tokens(
            state.output_token_logprobs_val,
            state.output_token_logprobs_idx,
            return_text_in_logprobs,
        )

        if top_logprobs_num > 0:
            if len(recv_obj.input_top_logprobs_val) > 0:
                state.input_top_logprobs_val.extend(
                    recv_obj.input_top_logprobs_val[recv_obj_index]
                )
                state.input_top_logprobs_idx.extend(
                    recv_obj.input_top_logprobs_idx[recv_obj_index]
                )
            state.output_top_logprobs_val.extend(
                recv_obj.output_top_logprobs_val[recv_obj_index]
            )
            state.output_top_logprobs_idx.extend(
                recv_obj.output_top_logprobs_idx[recv_obj_index]
            )
            meta_info["input_top_logprobs"] = self.detokenize_top_logprobs_tokens(
                state.input_top_logprobs_val,
                state.input_top_logprobs_idx,
                return_text_in_logprobs,
            )
            meta_info["output_top_logprobs"] = self.detokenize_top_logprobs_tokens(
                state.output_top_logprobs_val,
                state.output_top_logprobs_idx,
                return_text_in_logprobs,
            )

        if token_ids_logprob is not None:
            if len(recv_obj.input_token_ids_logprobs_val) > 0:
                state.input_token_ids_logprobs_val.extend(
                    recv_obj.input_token_ids_logprobs_val[recv_obj_index]
                )
                state.input_token_ids_logprobs_idx.extend(
                    recv_obj.input_token_ids_logprobs_idx[recv_obj_index]
                )
            state.output_token_ids_logprobs_val.extend(
                recv_obj.output_token_ids_logprobs_val[recv_obj_index]
            )
            state.output_token_ids_logprobs_idx.extend(
                recv_obj.output_token_ids_logprobs_idx[recv_obj_index]
            )
            meta_info["input_token_ids_logprobs"] = self.detokenize_top_logprobs_tokens(
                state.input_token_ids_logprobs_val,
                state.input_token_ids_logprobs_idx,
                return_text_in_logprobs,
            )
            meta_info["output_token_ids_logprobs"] = (
                self.detokenize_top_logprobs_tokens(
                    state.output_token_ids_logprobs_val,
                    state.output_token_ids_logprobs_idx,
                    return_text_in_logprobs,
                )
            )

    def detokenize_logprob_tokens(
        self,
        token_logprobs_val: List[float],
        token_logprobs_idx: List[int],
        decode_to_text: bool,
    ):
        if not decode_to_text:
            return [
                (logprob, token_id, None)
                for logprob, token_id in zip(token_logprobs_val, token_logprobs_idx)
            ]
        else:
            assert self.tokenizer is not None
            token_texts = self.tokenizer.batch_decode(token_logprobs_idx)
            return list(zip(token_logprobs_val, token_logprobs_idx, token_texts))

    def detokenize_top_logprobs_tokens(
        self,
        token_logprobs_val: List[float],
        token_logprobs_idx: List[int],
        decode_to_text: bool,
    ):
        # TODO: The current implementation only batches the detokenization for top-k tokens per single position.
        # We should batch all top-k tokens in all positions.
        ret = []
        for i in range(len(token_logprobs_val)):
            if token_logprobs_val[i]:
                ret.append(
                    self.detokenize_logprob_tokens(
                        token_logprobs_val[i], token_logprobs_idx[i], decode_to_text
                    )
                )
            else:
                ret.append(None)
        return ret

    def collect_metrics(self, state: ReqState, recv_obj: BatchStrOut, i: int):
        completion_tokens = (
            recv_obj.completion_tokens[i]
            if getattr(recv_obj, "completion_tokens", None)
            else 0
        )

        if (
            state.first_token_time == 0.0
            and self.disaggregation_mode != DisaggregationMode.PREFILL
        ):
            state.first_token_time = state.last_time = time.time()
            state.last_completion_tokens = completion_tokens
            self.metrics_collector.observe_time_to_first_token(
                state.first_token_time - state.created_time
            )
        else:
            num_new_tokens = completion_tokens - state.last_completion_tokens
            if num_new_tokens:
                new_time = time.time()
                interval = new_time - state.last_time
                self.metrics_collector.observe_inter_token_latency(
                    interval,
                    num_new_tokens,
                )
                state.last_time = new_time
                state.last_completion_tokens = completion_tokens

        if state.finished:
            has_grammar = (
                state.obj.sampling_params.get("json_schema", None)
                or state.obj.sampling_params.get("regex", None)
                or state.obj.sampling_params.get("ebnf", None)
                or state.obj.sampling_params.get("structural_tag", None)
            )
            self.metrics_collector.observe_one_finished_request(
                recv_obj.prompt_tokens[i],
                completion_tokens,
                recv_obj.cached_tokens[i],
                state.finished_time - state.created_time,
                has_grammar,
            )

    def dump_requests(self, state: ReqState, out_dict: dict):
        self.dump_request_list.append(
            (state.obj, out_dict, state.created_time, time.time())
        )

        if len(self.dump_request_list) >= self.dump_requests_threshold:
            filename = os.path.join(
                self.dump_requests_folder,
                datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".pkl",
            )
            self._dump_data_to_file(
                data_list=self.dump_request_list,
                filename=filename,
                log_message=f"Dump {len(self.dump_request_list)} requests to {filename}",
            )
            self.dump_request_list = []

    def record_request_for_crash_dump(self, state: ReqState, out_dict: dict):
        current_time = time.time()
        self.crash_dump_request_list.append(
            (state.obj, out_dict, state.created_time, current_time)
        )
        # Remove requests older than 5 minutes based on finish time
        while (
            self.crash_dump_request_list
            and current_time - self.crash_dump_request_list[0][3] >= 300
        ):
            self.crash_dump_request_list.popleft()

    def _dump_data_to_file(
        self, data_list: List[Tuple], filename: str, log_message: str
    ):
        logger.info(log_message)
        to_dump_with_server_args = {
            "server_args": self.server_args,
            "requests": data_list.copy(),
        }

        def background_task():
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            with open(filename, "wb") as f:
                pickle.dump(to_dump_with_server_args, f)

        asyncio.create_task(asyncio.to_thread(background_task))

    def _handle_abort_req(self, recv_obj):
        state = self.rid_to_state[recv_obj.rid]
        state.finished = True
        if recv_obj.finished_reason:
            out = {
                "meta_info": {
                    "id": recv_obj.rid,
                    "finish_reason": recv_obj.finished_reason,
                },
            }
        else:
            out = {
                "text": "",
                "meta_info": {
                    "id": recv_obj.rid,
                    "finish_reason": {
                        "type": "abort",
                        "message": "Abort before prefill",
                    },
                    "prompt_tokens": 0,
                    "completion_tokens": 0,
                },
            }
        state.out_list.append(out)
        state.event.set()

    def _handle_open_session_req_output(self, recv_obj):
        self.session_futures[recv_obj.session_id].set_result(
            recv_obj.session_id if recv_obj.success else None
        )

    def _handle_update_weights_from_disk_req_output(self, recv_obj):
        if self.server_args.dp_size == 1:
            self.model_update_result.set_result(recv_obj)
        else:  # self.server_args.dp_size > 1
            self.model_update_tmp.append(recv_obj)
            # set future if the all results are received
            if len(self.model_update_tmp) == self.server_args.dp_size:
                self.model_update_result.set_result(self.model_update_tmp)

    async def score_request(
        self,
        query: Optional[Union[str, List[int]]] = None,
        items: Optional[Union[str, List[str], List[List[int]]]] = None,
        label_token_ids: Optional[List[int]] = None,
        apply_softmax: bool = False,
        item_first: bool = False,
        request: Optional[Any] = None,
    ) -> List[List[float]]:
        """
        See Engine.score() for more details.
        """
        if label_token_ids is None:
            raise ValueError("label_token_ids must be provided")

        if self.tokenizer is not None:
            vocab_size = self.tokenizer.vocab_size
            for token_id in label_token_ids:
                if token_id >= vocab_size:
                    raise ValueError(
                        f"Token ID {token_id} is out of vocabulary (vocab size: {vocab_size})"
                    )

        # Handle string or tokenized query/items
        if isinstance(query, str) and (
            isinstance(items, str)
            or (isinstance(items, list) and (not items or isinstance(items[0], str)))
        ):
            # Both query and items are text
            items_list = [items] if isinstance(items, str) else items
            if item_first:
                prompts = [f"{item}{query}" for item in items_list]
            else:
                prompts = [f"{query}{item}" for item in items_list]
            batch_request = GenerateReqInput(
                text=prompts,
                return_logprob=True,
                token_ids_logprob=label_token_ids,
                stream=False,
                sampling_params={"max_new_tokens": 1},
            )
        elif (
            isinstance(query, list)
            and isinstance(items, list)
            and items
            and isinstance(items[0], list)
        ):
            # Both query and items are token IDs
            if item_first:
                input_ids_list = [item + query for item in items]
            else:
                input_ids_list = [query + item for item in items]
            batch_request = GenerateReqInput(
                input_ids=input_ids_list,
                return_logprob=True,
                token_ids_logprob=label_token_ids,
                stream=False,
                sampling_params={"max_new_tokens": 1},
            )
        else:
            raise ValueError(
                "Invalid combination of query/items types for score_request."
            )

        results = await self.generate_request(batch_request, request).__anext__()
        scores = []

        for result in results:
            # Get logprobs for each token
            logprobs = {}
            for logprob, token_id, _ in result["meta_info"].get(
                "output_token_ids_logprobs", []
            )[0]:
                if token_id in label_token_ids:
                    logprobs[token_id] = logprob

            # Get scores in order of label_token_ids
            score_list = [
                logprobs.get(token_id, float("-inf")) for token_id in label_token_ids
            ]

            # Apply softmax to logprobs if needed
            if apply_softmax:
                score_list = torch.softmax(torch.tensor(score_list), dim=0).tolist()
            else:
                # Convert logprobs to probabilities if not using softmax
                score_list = [
                    math.exp(x) if x != float("-inf") else 0.0 for x in score_list
                ]

            scores.append(score_list)

        return scores


def _determine_tensor_transport_mode(server_args: ServerArgs) -> TensorTransportMode:
    is_cross_node = server_args.dist_init_addr

    if is_cross_node:
        # Fallback to default CPU transport for multi-node
        return "default"
    else:
        return "cuda_ipc"


async def print_exception_wrapper(func):
    """
    Sometimes an asyncio function does not print exception.
    We do another wrapper to handle the exception.
    """
    try:
        await func()
    except Exception:
        traceback = get_exception_traceback()
        logger.error(f"TokenizerManager hit an exception: {traceback}")
        if hasattr(func, "__self__") and isinstance(func.__self__, TokenizerManager):
            func.__self__.dump_requests_before_crash()
        kill_process_tree(os.getpid(), include_parent=True)
        sys.exit(1)


class SignalHandler:
    def __init__(self, tokenizer_manager: TokenizerManager):
        self.tokenizer_manager = tokenizer_manager

    def sigterm_handler(self, signum=None, frame=None):
        logger.warning(
            f"SIGTERM received. {signum=} {frame=}. Draining requests and shutting down..."
        )
        self.tokenizer_manager.gracefully_exit = True

    def running_phase_sigquit_handler(self, signum=None, frame=None):
        logger.error(
            "Received sigquit from a child process. It usually means the child failed."
        )
        self.tokenizer_manager.dump_requests_before_crash()
        kill_process_tree(os.getpid())


T = TypeVar("T")


class _Communicator(Generic[T]):
    """Note: The communicator now only run up to 1 in-flight request at any time."""

    def __init__(self, sender, fan_out: int):
        self._sender = sender
        self._fan_out = fan_out
        self._result_event: Optional[asyncio.Event] = None
        self._result_values: Optional[List[T]] = None
        self._ready_queue: Deque[asyncio.Future] = deque()

    async def __call__(self, obj):
        ready_event = asyncio.Event()
        if self._result_event is not None or len(self._ready_queue) > 0:
            self._ready_queue.append(ready_event)
            await ready_event.wait()
            assert self._result_event is None
            assert self._result_values is None

        if obj:
            self._sender.send_pyobj(obj)

        self._result_event = asyncio.Event()
        self._result_values = []
        await self._result_event.wait()
        result_values = self._result_values
        self._result_event = self._result_values = None

        if len(self._ready_queue) > 0:
            self._ready_queue.popleft().set()

        return result_values

    def handle_recv(self, recv_obj: T):
        self._result_values.append(recv_obj)
        if len(self._result_values) == self._fan_out:
            self._result_event.set()


# Note: request abort handling logic
# We should handle all of the following cases correctly.
#
# | entrypoint | is_streaming | status          | abort engine    | cancel asyncio task   | rid_to_state                |
# | ---------- | ------------ | --------------- | --------------- | --------------------- | --------------------------- |
# | http       | yes          | validation      | background task | fast api              | del in _handle_abort_req    |
# | http       | yes          | waiting queue   | background task | fast api              | del in _handle_abort_req    |
# | http       | yes          | running         | background task | fast api              | del in _handle_batch_output |
# | http       | no           | validation      | http exception  | http exception        | del in _handle_abort_req    |
# | http       | no           | waiting queue   | type 1          | type 1 exception      | del in _handle_abort_req    |
# | http       | no           | running         | type 3          | type 3 exception      | del in _handle_batch_output |
#