scheduler.py 77.8 KB
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# 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.
# ==============================================================================
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"""A scheduler that manages a tensor parallel GPU worker."""

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import faulthandler
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import logging
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import os
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import signal
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import sys
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import threading
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import time
import warnings
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from collections import defaultdict, deque
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from concurrent import futures
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from dataclasses import dataclass
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from http import HTTPStatus
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from types import SimpleNamespace
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from typing import Dict, List, Optional, Tuple, Union
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import psutil
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import setproctitle
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import torch
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import zmq
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from torch.distributed import barrier
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from sglang.global_config import global_config
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.constrained.base_grammar_backend import create_grammar_backend
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from sglang.srt.disaggregation.decode import (
    DecodePreallocQueue,
    DecodeTransferQueue,
    SchedulerDisaggregationDecodeMixin,
)
from sglang.srt.disaggregation.prefill import (
    PrefillBootstrapQueue,
    SchedulerDisaggregationPrefillMixin,
)
from sglang.srt.disaggregation.utils import (
    DisaggregationMode,
    ReqToMetadataIdxAllocator,
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    TransferBackend,
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)
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
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from sglang.srt.managers.io_struct import (
    AbortReq,
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    CloseSessionReqInput,
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    ExpertDistributionReq,
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    ExpertDistributionReqOutput,
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    FlushCacheReq,
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    GetInternalStateReq,
    GetInternalStateReqOutput,
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    GetWeightsByNameReqInput,
    GetWeightsByNameReqOutput,
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    HealthCheckOutput,
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    InitWeightsUpdateGroupReqInput,
    InitWeightsUpdateGroupReqOutput,
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    OpenSessionReqInput,
    OpenSessionReqOutput,
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    ProfileReq,
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    ProfileReqOutput,
    ProfileReqType,
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    ReleaseMemoryOccupationReqInput,
    ReleaseMemoryOccupationReqOutput,
    ResumeMemoryOccupationReqInput,
    ResumeMemoryOccupationReqOutput,
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    RpcReqInput,
    RpcReqOutput,
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    SetInternalStateReq,
    SetInternalStateReqOutput,
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    TokenizedEmbeddingReqInput,
    TokenizedGenerateReqInput,
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    UpdateWeightFromDiskReqInput,
    UpdateWeightFromDiskReqOutput,
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    UpdateWeightsFromDistributedReqInput,
    UpdateWeightsFromDistributedReqOutput,
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    UpdateWeightsFromTensorReqInput,
    UpdateWeightsFromTensorReqOutput,
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)
from sglang.srt.managers.schedule_batch import (
    FINISH_ABORT,
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    MultimodalInputs,
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    Req,
    ScheduleBatch,
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    global_server_args_dict,
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)
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from sglang.srt.managers.schedule_policy import (
    AddReqResult,
    PrefillAdder,
    SchedulePolicy,
)
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from sglang.srt.managers.scheduler_output_processor_mixin import (
    SchedulerOutputProcessorMixin,
)
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from sglang.srt.managers.session_controller import Session
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from sglang.srt.managers.tp_worker import TpModelWorker
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from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient
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from sglang.srt.managers.utils import validate_input_length
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from sglang.srt.mem_cache.chunk_cache import ChunkCache
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from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
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from sglang.srt.mem_cache.radix_cache import RadixCache
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from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.reasoning_parser import ReasoningParser
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from sglang.srt.server_args import PortArgs, ServerArgs
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
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from sglang.srt.utils import (
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    DynamicGradMode,
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    broadcast_pyobj,
    configure_logger,
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    crash_on_warnings,
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    get_bool_env_var,
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    get_zmq_socket,
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    kill_itself_when_parent_died,
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    pyspy_dump_schedulers,
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    set_gpu_proc_affinity,
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    set_random_seed,
    suppress_other_loggers,
)
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from sglang.utils import TypeBasedDispatcher, get_exception_traceback
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expert_distribution_recorder = ExpertDistributionRecorder()

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logger = logging.getLogger(__name__)

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# Test retract decode for debugging purposes
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TEST_RETRACT = get_bool_env_var("SGLANG_TEST_RETRACT")
RECORD_STEP_TIME = get_bool_env_var("SGLANG_RECORD_STEP_TIME")
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@dataclass
class GenerationBatchResult:
    logits_output: LogitsProcessorOutput
    next_token_ids: List[int]
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    extend_input_len_per_req: List[int]
    extend_logprob_start_len_per_req: List[int]
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    bid: int


@dataclass
class EmbeddingBatchResult:
    embeddings: torch.Tensor
    bid: int


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class Scheduler(
    SchedulerOutputProcessorMixin,
    SchedulerDisaggregationDecodeMixin,
    SchedulerDisaggregationPrefillMixin,
):
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    """A scheduler that manages a tensor parallel GPU worker."""

    def __init__(
        self,
        server_args: ServerArgs,
        port_args: PortArgs,
        gpu_id: int,
        tp_rank: int,
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        dp_rank: Optional[int],
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    ):
        # Parse args
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        self.server_args = server_args
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        self.tp_rank = tp_rank
        self.tp_size = server_args.tp_size
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        self.schedule_policy = server_args.schedule_policy
        self.lora_paths = server_args.lora_paths
        self.max_loras_per_batch = server_args.max_loras_per_batch
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        self.enable_overlap = not server_args.disable_overlap_schedule
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        self.skip_tokenizer_init = server_args.skip_tokenizer_init
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        self.enable_metrics = server_args.enable_metrics
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        self.stream_interval = server_args.stream_interval
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        self.spec_algorithm = SpeculativeAlgorithm.from_string(
            server_args.speculative_algorithm
        )
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        self.gpu_id = gpu_id
        self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
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        self.page_size = server_args.page_size
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        # Distributed rank info
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        self.dp_size = server_args.dp_size
        self.attn_tp_rank, self.attn_tp_size, self.dp_rank = (
            compute_dp_attention_world_info(
                server_args.enable_dp_attention,
                self.tp_rank,
                self.tp_size,
                self.dp_size,
            )
        )

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        # Init inter-process communication
        context = zmq.Context(2)
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        if self.attn_tp_rank == 0:
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            self.recv_from_tokenizer = get_zmq_socket(
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                context, zmq.PULL, port_args.scheduler_input_ipc_name, False
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            )
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            self.send_to_tokenizer = get_zmq_socket(
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                context, zmq.PUSH, port_args.tokenizer_ipc_name, False
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            )
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            if server_args.skip_tokenizer_init:
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                # Directly send to the TokenizerManager
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                self.send_to_detokenizer = get_zmq_socket(
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                    context, zmq.PUSH, port_args.tokenizer_ipc_name, False
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                )
            else:
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                # Send to the DetokenizerManager
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                self.send_to_detokenizer = get_zmq_socket(
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                    context, zmq.PUSH, port_args.detokenizer_ipc_name, False
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                )
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            self.recv_from_rpc = get_zmq_socket(
                context, zmq.DEALER, port_args.rpc_ipc_name, False
            )
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        else:
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            self.recv_from_tokenizer = None
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            self.recv_from_rpc = None
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            self.send_to_tokenizer = SimpleNamespace(send_pyobj=lambda x: None)
            self.send_to_detokenizer = SimpleNamespace(send_pyobj=lambda x: None)
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        # Init tokenizer
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        self.init_tokenizer()
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        # Set reasoning_parser and think_end_id if --reasoning_parser is enabled
        if self.server_args.reasoning_parser and self.tokenizer:
            reasoning_parser = ReasoningParser(
                model_type=self.server_args.reasoning_parser, stream_reasoning=False
            )
            self.tokenizer.think_end_id = self.tokenizer.encode(
                reasoning_parser.detector.think_end_token, add_special_tokens=False
            )[0]

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        # Check whether overlap can be enabled
        if not self.is_generation:
            self.enable_overlap = False
            logger.info("Overlap scheduler is disabled for embedding models.")
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        if self.model_config.is_multimodal:
            self.enable_overlap = False
            logger.info("Overlap scheduler is disabled for multimodal models.")

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        # Launch a tensor parallel worker
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        if self.enable_overlap:
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            TpWorkerClass = TpModelWorkerClient
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        else:
            TpWorkerClass = TpModelWorker
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        self.tp_worker = TpWorkerClass(
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            server_args=server_args,
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            gpu_id=gpu_id,
            tp_rank=tp_rank,
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            dp_rank=dp_rank,
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            nccl_port=port_args.nccl_port,
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        )
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        # Launch a draft worker for speculative decoding
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        if self.spec_algorithm.is_eagle():
            from sglang.srt.speculative.eagle_worker import EAGLEWorker

            self.draft_worker = EAGLEWorker(
                gpu_id=gpu_id,
                tp_rank=tp_rank,
                server_args=server_args,
                nccl_port=port_args.nccl_port,
                target_worker=self.tp_worker,
                dp_rank=dp_rank,
            )
        else:
            self.draft_worker = None

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        # Get token and memory info from the model worker
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        (
            self.max_total_num_tokens,
            self.max_prefill_tokens,
            self.max_running_requests,
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            self.max_req_len,
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            self.max_req_input_len,
            self.random_seed,
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            self.device,
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            worker_global_server_args_dict,
            _,
            _,
            _,
        ) = self.tp_worker.get_worker_info()
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        self.tp_cpu_group = self.tp_worker.get_tp_cpu_group()
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        self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group()
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        self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
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        global_server_args_dict.update(worker_global_server_args_dict)
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        set_random_seed(self.random_seed)
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        # Print debug info
        logger.info(
            f"max_total_num_tokens={self.max_total_num_tokens}, "
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            f"chunked_prefill_size={server_args.chunked_prefill_size}, "
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            f"max_prefill_tokens={self.max_prefill_tokens}, "
            f"max_running_requests={self.max_running_requests}, "
            f"context_len={self.model_config.context_len}"
        )

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        # Init memory pool and cache
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        self.init_memory_pool_and_cache()
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        # Init running status
        self.waiting_queue: List[Req] = []
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        # The running decoding batch for continuous batching
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        self.running_batch: ScheduleBatch = ScheduleBatch(reqs=[], batch_is_full=False)
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        # The current forward batch
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        self.cur_batch: Optional[ScheduleBatch] = None
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        # The last forward batch
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        self.last_batch: Optional[ScheduleBatch] = None
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        self.forward_ct = 0
        self.forward_ct_decode = 0
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        self.num_generated_tokens = 0
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        self.num_prefill_tokens = 0
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        self.last_decode_stats_tic = time.time()
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        self.last_prefill_stats_tic = time.time()
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        self.return_health_check_ct = 0
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        self.current_stream = torch.get_device_module(self.device).current_stream()
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        if self.device == "cpu":
            self.current_stream.synchronize = lambda: None  # No-op for CPU
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        # Init session info
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        self.sessions: Dict[str, Session] = {}
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        # Init chunked prefill
        self.chunked_prefill_size = server_args.chunked_prefill_size
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        if self.chunked_prefill_size <= 0:  # -1 means disable
            self.chunked_prefill_size = None
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        self.chunked_req = None
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        self.is_mixed_chunk = (
            self.chunked_prefill_size is not None and server_args.enable_mixed_chunk
        )

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        # Init the grammar backend for constrained generation
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        self.grammar_queue: List[Req] = []
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        if not server_args.skip_tokenizer_init:
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            self.grammar_backend = create_grammar_backend(
                server_args, self.tokenizer, self.model_config.vocab_size
            )
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        else:
            self.grammar_backend = None
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        # Init schedule policy and new token estimation
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        self.policy = SchedulePolicy(
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            self.schedule_policy,
            self.tree_cache,
            self.enable_hierarchical_cache,
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        )
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        assert (
            server_args.schedule_conservativeness >= 0
        ), "Invalid schedule_conservativeness"
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        self.init_new_token_ratio = min(
            global_config.default_init_new_token_ratio
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            * server_args.schedule_conservativeness,
            1.0,
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        )
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        self.min_new_token_ratio = min(
            self.init_new_token_ratio
            * global_config.default_min_new_token_ratio_factor,
            1.0,
        )
        self.new_token_ratio_decay = (
            self.init_new_token_ratio - self.min_new_token_ratio
        ) / global_config.default_new_token_ratio_decay_steps
        self.new_token_ratio = self.init_new_token_ratio

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        # Init watchdog thread
        self.watchdog_timeout = server_args.watchdog_timeout
        t = threading.Thread(target=self.watchdog_thread, daemon=True)
        t.start()
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        self.parent_process = psutil.Process().parent()
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        # Init memory saver
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        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
            enable=server_args.enable_memory_saver
        )

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        # Init profiler
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        self.torch_profiler = None
        self.torch_profiler_output_dir: Optional[str] = None
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        self.profiler_activities: Optional[List[str]] = None
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        self.profiler_target_forward_ct: Optional[int] = None
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        # Init metrics stats
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        self.init_metrics()
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        # Init request dispatcher
        self._request_dispatcher = TypeBasedDispatcher(
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            [
                (TokenizedGenerateReqInput, self.handle_generate_request),
                (TokenizedEmbeddingReqInput, self.handle_embedding_request),
                (FlushCacheReq, self.flush_cache_wrapped),
                (AbortReq, self.abort_request),
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                (OpenSessionReqInput, self.open_session),
                (CloseSessionReqInput, self.close_session),
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                (UpdateWeightFromDiskReqInput, self.update_weights_from_disk),
                (InitWeightsUpdateGroupReqInput, self.init_weights_update_group),
                (
                    UpdateWeightsFromDistributedReqInput,
                    self.update_weights_from_distributed,
                ),
                (UpdateWeightsFromTensorReqInput, self.update_weights_from_tensor),
                (GetWeightsByNameReqInput, self.get_weights_by_name),
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                (ReleaseMemoryOccupationReqInput, self.release_memory_occupation),
                (ResumeMemoryOccupationReqInput, self.resume_memory_occupation),
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                (ProfileReq, self.profile),
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                (GetInternalStateReq, self.get_internal_state),
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                (SetInternalStateReq, self.set_internal_state),
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                (RpcReqInput, self.handle_rpc_request),
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                (ExpertDistributionReq, self.expert_distribution_handle),
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            ]
        )

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        self.disaggregation_mode = DisaggregationMode(
            self.server_args.disaggregation_mode
        )
        self.init_disaggregation()

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    def init_tokenizer(self):
        server_args = self.server_args
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        self.model_config = ModelConfig(
            server_args.model_path,
            trust_remote_code=server_args.trust_remote_code,
            revision=server_args.revision,
            context_length=server_args.context_length,
            model_override_args=server_args.json_model_override_args,
            is_embedding=server_args.is_embedding,
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            enable_multimodal=server_args.enable_multimodal,
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            dtype=server_args.dtype,
            quantization=server_args.quantization,
        )
        self.is_generation = self.model_config.is_generation
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        if server_args.skip_tokenizer_init:
            self.tokenizer = self.processor = None
        else:
            if self.model_config.is_multimodal:
                self.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,
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                    use_fast=not server_args.disable_fast_image_processor,
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                )
                self.tokenizer = self.processor.tokenizer
            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,
                )

    def init_memory_pool_and_cache(self):
        server_args = self.server_args

        self.req_to_token_pool, self.token_to_kv_pool_allocator = (
            self.tp_worker.get_memory_pool()
        )

        if (
            server_args.chunked_prefill_size is not None
            and server_args.disable_radix_cache
        ):
            self.tree_cache = ChunkCache(
                req_to_token_pool=self.req_to_token_pool,
                token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
            )
        else:
            if self.enable_hierarchical_cache:
                self.tree_cache = HiRadixCache(
                    req_to_token_pool=self.req_to_token_pool,
                    token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
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                    tp_cache_group=self.tp_cpu_group,
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                    page_size=self.page_size,
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                    hicache_ratio=server_args.hicache_ratio,
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                )
            else:
                self.tree_cache = RadixCache(
                    req_to_token_pool=self.req_to_token_pool,
                    token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
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                    page_size=self.page_size,
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                    disable=server_args.disable_radix_cache,
                )

        self.decode_mem_cache_buf_multiplier = (
            1
            if self.spec_algorithm.is_none()
            else (
                server_args.speculative_num_draft_tokens
                + (
                    server_args.speculative_eagle_topk
                    * server_args.speculative_num_steps
                )
            )
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        )
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    def init_metrics(self):
        # The largest prefill length of a single request
        self._largest_prefill_len: int = 0
        # The largest context length (prefill + generation) of a single request
        self._largest_prefill_decode_len: int = 0
        self.last_gen_throughput: float = 0.0
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        self.last_input_throughput: float = 0.0
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        self.step_time_dict = defaultdict(list)  # Dict[batch size -> step time]
        self.spec_num_total_accepted_tokens = 0
        self.spec_num_total_forward_ct = 0
        self.cum_spec_accept_length = 0
        self.cum_spec_accept_count = 0
        self.stats = SchedulerStats()
        if self.enable_metrics:
            engine_type = "unified"
            self.metrics_collector = SchedulerMetricsCollector(
                labels={
                    "model_name": self.server_args.served_model_name,
                    "engine_type": engine_type,
                },
            )
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    def init_disaggregation(self):
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        self.transfer_backend = TransferBackend(
            self.server_args.disaggregation_transfer_backend
        )

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        if (
            self.disaggregation_mode == DisaggregationMode.DECODE
        ):  # *2 for the headroom.
            buffer_size = (self.req_to_token_pool.size) * 2
            req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
                buffer_size
            )
            aux_dtype = torch.int32
            # A list of metadata buffers. The shape is (b, metadata_size) where
            # b corresponds to a max running requests. The last shape * dtype.itemsize
            # should be larger than 64 bytes to work with RDMA, so we pad it.
            output_id_buffer = torch.zeros(
                (buffer_size, 16), dtype=aux_dtype, device="cpu"
            )
            metadata_buffers = [output_id_buffer]

            # The decode requests polling kv cache
            self.disagg_decode_transfer_queue = DecodeTransferQueue(
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                gloo_group=self.attn_tp_cpu_group,
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                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
                metadata_buffers=metadata_buffers,
            )

            # The decode requests pending for pre-allocation
            self.disagg_decode_prealloc_queue = DecodePreallocQueue(
                req_to_token_pool=self.req_to_token_pool,
                token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
                metadata_buffers=metadata_buffers,
                aux_dtype=aux_dtype,
                scheduler=self,
                transfer_queue=self.disagg_decode_transfer_queue,
                tree_cache=self.tree_cache,
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                gloo_group=self.attn_tp_cpu_group,
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                tp_rank=self.tp_rank,
                tp_size=self.tp_size,
                bootstrap_port=self.server_args.disaggregation_bootstrap_port,
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                transfer_backend=self.transfer_backend,
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            )
        elif self.disaggregation_mode == DisaggregationMode.PREFILL:
            # *2 for the headroom.
            buffer_size = self.max_running_requests * 2
            req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
                buffer_size
            )
            aux_dtype = torch.int32
            # A list of metadata buffers. The shape is (b, metadata_size) where
            # b corresponds to a max running requests. The last shape * dtype.itemsize
            # should be larger than 64 bytes to work with RDMA, so we pad it.
            output_id_buffer = torch.zeros(
                (buffer_size, 16), dtype=aux_dtype, device="cpu"
            )
            metadata_buffers = [output_id_buffer]

            self.disagg_prefill_pending_queue = PrefillBootstrapQueue(
                token_to_kv_pool=self.token_to_kv_pool_allocator.get_kvcache(),
                req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
                metadata_buffers=metadata_buffers,
                aux_dtype=aux_dtype,
                tp_rank=self.tp_rank,
                tp_size=self.tp_size,
                bootstrap_port=self.server_args.disaggregation_bootstrap_port,
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                gloo_group=self.attn_tp_cpu_group,
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                transfer_backend=self.transfer_backend,
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                scheduler=self,
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            )
            # The prefill requests that are in the middle of kv sending
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            self.disagg_prefill_inflight_queue: List[Req] = []
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    @DynamicGradMode()
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    def event_loop_normal(self):
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        """A normal scheduler loop."""
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        while True:
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            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)
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            batch = self.get_next_batch_to_run()
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            self.cur_batch = batch
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            if batch:
                result = self.run_batch(batch)
                self.process_batch_result(batch, result)
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            else:
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                # When the server is idle, do self-check and re-init some states
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                self.check_memory()
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                self.new_token_ratio = self.init_new_token_ratio
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            self.last_batch = batch
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    @DynamicGradMode()
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    def event_loop_overlap(self):
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        """A scheduler loop that overlaps the CPU processing and GPU computation."""
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        self.result_queue = deque()
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        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)

            batch = self.get_next_batch_to_run()
            self.cur_batch = batch
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            if batch:
                result = self.run_batch(batch)
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                self.result_queue.append((batch.copy(), result))
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                if self.last_batch is None:
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                    # Create a dummy first batch to start the pipeline for overlap schedule.
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                    # It is now used for triggering the sampling_info_done event.
                    tmp_batch = ScheduleBatch(
                        reqs=None,
                        forward_mode=ForwardMode.DUMMY_FIRST,
                        next_batch_sampling_info=self.tp_worker.cur_sampling_info,
                    )
                    self.process_batch_result(tmp_batch, None)

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            if self.last_batch:
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                # Process the results of the last batch
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                tmp_batch, tmp_result = self.result_queue.popleft()
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                tmp_batch.next_batch_sampling_info = (
                    self.tp_worker.cur_sampling_info if batch else None
                )
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                self.process_batch_result(tmp_batch, tmp_result)
            elif batch is None:
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                # When the server is idle, do self-check and re-init some states
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                self.check_memory()
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                self.new_token_ratio = self.init_new_token_ratio
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            self.last_batch = batch

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    def recv_requests(self) -> List[Req]:
        """Receive results at tp_rank = 0 and broadcast it to all other TP ranks."""
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        if self.attn_tp_rank == 0:
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            recv_reqs = []

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            while True:
                try:
                    recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
                except zmq.ZMQError:
                    break
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                recv_reqs.append(recv_req)
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            while True:
                try:
                    recv_rpc = self.recv_from_rpc.recv_pyobj(zmq.NOBLOCK)
                except zmq.ZMQError:
                    break
                recv_reqs.append(recv_rpc)
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        else:
            recv_reqs = None
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        if self.server_args.enable_dp_attention:
            if self.attn_tp_rank == 0:
                work_reqs = [
                    req
                    for req in recv_reqs
                    if isinstance(
                        req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
                    )
                ]
                control_reqs = [
                    req
                    for req in recv_reqs
                    if not isinstance(
                        req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
                    )
                ]
            else:
                work_reqs = None
                control_reqs = None

            if self.attn_tp_size != 1:
                attn_tp_rank_0 = self.dp_rank * self.attn_tp_size
                work_reqs = broadcast_pyobj(
                    work_reqs,
                    self.attn_tp_rank,
                    self.attn_tp_cpu_group,
                    src=attn_tp_rank_0,
                )
            if self.tp_size != 1:
                control_reqs = broadcast_pyobj(
                    control_reqs, self.tp_rank, self.tp_cpu_group
                )
            recv_reqs = work_reqs + control_reqs
        elif self.tp_size != 1:
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            recv_reqs = broadcast_pyobj(recv_reqs, self.tp_rank, self.tp_cpu_group)
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        return recv_reqs

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    def process_input_requests(self, recv_reqs: List):
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        for recv_req in recv_reqs:
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            # If it is a health check generation request and there are running requests, ignore it.
            if is_health_check_generate_req(recv_req) and (
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                self.chunked_req is not None or not self.running_batch.is_empty()
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            ):
                self.return_health_check_ct += 1
                continue

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            output = self._request_dispatcher(recv_req)
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            if output is not None:
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                if isinstance(output, RpcReqOutput):
                    if self.recv_from_rpc is not None:
                        self.recv_from_rpc.send_pyobj(output)
                else:
                    self.send_to_tokenizer.send_pyobj(output)
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    def handle_generate_request(
        self,
        recv_req: TokenizedGenerateReqInput,
    ):
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        # Create a new request
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        if (
            recv_req.session_params is None
            or recv_req.session_params.id is None
            or recv_req.session_params.id not in self.sessions
        ):
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            if recv_req.input_embeds is not None:
                # Generate fake input_ids based on the length of input_embeds
                seq_length = len(recv_req.input_embeds)
                fake_input_ids = [1] * seq_length
                recv_req.input_ids = fake_input_ids

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            # Handle custom logit processor passed to the request
            custom_logit_processor = recv_req.custom_logit_processor
            if (
                not self.server_args.enable_custom_logit_processor
                and custom_logit_processor is not None
            ):
                logger.warning(
                    "The SGLang server is not configured to enable custom logit processor."
                    "The custom logit processor passed in will be ignored."
                    "Please set --enable-custom-logits-processor to enable this feature."
                )
                custom_logit_processor = None

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            req = Req(
                recv_req.rid,
                recv_req.input_text,
                recv_req.input_ids,
                recv_req.sampling_params,
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                return_logprob=recv_req.return_logprob,
                top_logprobs_num=recv_req.top_logprobs_num,
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                token_ids_logprob=recv_req.token_ids_logprob,
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                stream=recv_req.stream,
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                lora_path=recv_req.lora_path,
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                input_embeds=recv_req.input_embeds,
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                custom_logit_processor=custom_logit_processor,
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                return_hidden_states=recv_req.return_hidden_states,
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                eos_token_ids=self.model_config.hf_eos_token_id,
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                bootstrap_host=recv_req.bootstrap_host,
                bootstrap_room=recv_req.bootstrap_room,
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            )
            req.tokenizer = self.tokenizer
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            if (
                recv_req.session_params is not None
                and recv_req.session_params.id is not None
            ):
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                req.finished_reason = FINISH_ABORT(
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                    f"Invalid request: session id {recv_req.session_params.id} does not exist"
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                )
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                self._add_request_to_queue(req)
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                return
        else:
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            # Create a new request from a previous session
            session = self.sessions[recv_req.session_params.id]
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            req = session.create_req(recv_req, self.tokenizer)
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            if isinstance(req.finished_reason, FINISH_ABORT):
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                self._add_request_to_queue(req)
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                return
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        # Handle multimodal inputs
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        if recv_req.mm_inputs is not None:
            image_inputs = MultimodalInputs.from_dict(recv_req.mm_inputs)
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            # Expand a single image token into multiple dummy tokens for receiving image embeddings
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            req.origin_input_ids = self.pad_input_ids_func(
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                req.origin_input_ids, image_inputs
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            )
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            req.extend_image_inputs(image_inputs)
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            if len(req.origin_input_ids) >= self.max_req_input_len:
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                error_msg = (
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                    "Multimodal prompt is too long after expanding multimodal tokens. "
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                    f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}."
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                )
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                logger.error(error_msg)
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                req.origin_input_ids = [0]
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                req.multimodal_inputs = None
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                req.sampling_params.max_new_tokens = 0
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                req.finished_reason = FINISH_ABORT(
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                    error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
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                )
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                self._add_request_to_queue(req)
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                return

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        # Validate prompts length
        error_msg = validate_input_length(
            req,
            self.max_req_input_len,
            self.server_args.allow_auto_truncate,
        )
        if error_msg:
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            req.origin_input_ids = [0]
            req.sampling_params.max_new_tokens = 0
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            self._add_request_to_queue(req)
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            return
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        # Copy more attributes
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        if recv_req.logprob_start_len == -1 or not recv_req.return_logprob:
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            # By default, only return the logprobs for output tokens
            req.logprob_start_len = len(req.origin_input_ids) - 1
        else:
            req.logprob_start_len = recv_req.logprob_start_len

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        if req.logprob_start_len >= len(req.origin_input_ids):
            req.finished_reason = FINISH_ABORT(
                f"logprob_start_len, ({req.logprob_start_len}) is higher than the number of input tokens ({len(req.origin_input_ids)}). Request with a lower logprob_start_len.",
                HTTPStatus.BAD_REQUEST,
                "BadRequestError",
            )
            req.logprob_start_len = len(req.origin_input_ids) - 1
            self._add_request_to_queue(req)
            return

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        req.sampling_params.max_new_tokens = min(
            (
                req.sampling_params.max_new_tokens
                if req.sampling_params.max_new_tokens is not None
                else 1 << 30
            ),
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            self.max_req_len - len(req.origin_input_ids) - 1,
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        )

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        # Init grammar cache for this request
        add_to_grammar_queue = False
        if (
            req.sampling_params.json_schema is not None
            or req.sampling_params.regex is not None
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            or req.sampling_params.ebnf is not None
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            or req.sampling_params.structural_tag is not None
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        ):
            assert self.grammar_backend is not None
            if req.sampling_params.json_schema is not None:
                key = ("json", req.sampling_params.json_schema)
            elif req.sampling_params.regex is not None:
                key = ("regex", req.sampling_params.regex)
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            elif req.sampling_params.ebnf is not None:
                key = ("ebnf", req.sampling_params.ebnf)
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            elif req.sampling_params.structural_tag:
                key = ("structural_tag", req.sampling_params.structural_tag)
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            req.grammar = self.grammar_backend.get_cached_value(key)
            if not req.grammar:
                req.grammar = self.grammar_backend.get_future_value(key)
                add_to_grammar_queue = True

        if add_to_grammar_queue:
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            self.grammar_queue.append(req)
        else:
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            self._add_request_to_queue(req)

    def _add_request_to_queue(self, req: Req):
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        req.queue_time_start = time.time()
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        if self.disaggregation_mode == DisaggregationMode.PREFILL:
            self.disagg_prefill_pending_queue.add(req)
        elif self.disaggregation_mode == DisaggregationMode.DECODE:
            self.disagg_decode_prealloc_queue.add(req)
        else:
            self.waiting_queue.append(req)

    def _extend_requests_to_queue(self, reqs: List[Req], is_retracted: bool = False):
        if self.disaggregation_mode == DisaggregationMode.DECODE:
            self.disagg_decode_prealloc_queue.extend(reqs)
        else:
            self.waiting_queue.extend(reqs)
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    def handle_embedding_request(
        self,
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        recv_req: TokenizedEmbeddingReqInput,
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    ):
        req = Req(
            recv_req.rid,
            recv_req.input_text,
            recv_req.input_ids,
            recv_req.sampling_params,
        )
        req.tokenizer = self.tokenizer

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        # Handle multimodal inputs
        if recv_req.image_inputs is not None:
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            image_inputs = MultimodalInputs.from_dict(recv_req.image_inputs)
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            # Expand a single image token into multiple dummy tokens for receiving image embeddings
            req.origin_input_ids = self.pad_input_ids_func(
                req.origin_input_ids, image_inputs
            )
            req.extend_image_inputs(image_inputs)

            if len(req.origin_input_ids) >= self.max_req_input_len:
                error_msg = (
                    "Multimodal prompt is too long after expanding multimodal tokens. "
                    f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}."
                )
                logger.error(error_msg)
                req.origin_input_ids = [0]
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                req.multimodal_inputs = None
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                req.sampling_params.max_new_tokens = 0
                req.finished_reason = FINISH_ABORT(
                    error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
                )
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                req.queue_time_start = time.time()
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                self.waiting_queue.append(req)
                return

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        # Validate prompts length
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        error_msg = validate_input_length(
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            req,
            self.max_req_input_len,
            self.server_args.allow_auto_truncate,
        )
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        if error_msg:
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            self._add_request_to_queue(req)
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            return
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        # Copy more attributes
        req.logprob_start_len = len(req.origin_input_ids) - 1
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        self._add_request_to_queue(req)
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    def log_prefill_stats(
        self,
        adder: PrefillAdder,
        can_run_list: List[Req],
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        running_bs: int,
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    ):
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        gap_latency = time.time() - self.last_prefill_stats_tic
        self.last_prefill_stats_tic = time.time()
        self.last_input_throughput = self.num_prefill_tokens / gap_latency
        self.num_prefill_tokens = 0

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        num_used = self.max_total_num_tokens - (
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            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
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        )
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        self._largest_prefill_len = max(
            self._largest_prefill_len, adder.log_input_tokens
        )
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        num_new_seq = len(can_run_list)
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        f = (
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            f"Prefill batch. "
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            f"#new-seq: {num_new_seq}, "
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            f"#new-token: {adder.log_input_tokens}, "
            f"#cached-token: {adder.log_hit_tokens}, "
            f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
            f"#running-req: {running_bs}, "
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            f"#queue-req: {len(self.waiting_queue)}, "
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        )
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        logger.info(f)
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        if self.enable_metrics:
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            cache_hit_rate = adder.log_hit_tokens / (
                adder.log_input_tokens + adder.log_hit_tokens
            )
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            self.stats.num_running_reqs = running_bs
            self.stats.num_used_tokens = num_used
            self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2)
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            self.stats.num_queue_reqs = len(self.waiting_queue)
            self.stats.cache_hit_rate = cache_hit_rate
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            total_queue_latency = 0
            for req in can_run_list:
                total_queue_latency += req.queue_time_end - req.queue_time_start
            self.stats.avg_request_queue_latency = total_queue_latency / num_new_seq

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            self.metrics_collector.log_stats(self.stats)

    def log_decode_stats(self):
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        gap_latency = time.time() - self.last_decode_stats_tic
        self.last_decode_stats_tic = time.time()
        self.last_gen_throughput = self.num_generated_tokens / gap_latency
        self.num_generated_tokens = 0
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        num_running_reqs = len(self.running_batch.reqs)
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        num_used = self.max_total_num_tokens - (
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            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
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        )
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        if RECORD_STEP_TIME:
            self.step_time_dict[num_running_reqs].append(
                gap_latency / self.server_args.decode_log_interval
            )
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        if self.spec_algorithm.is_none():
            msg = (
                f"Decode batch. "
                f"#running-req: {num_running_reqs}, "
                f"#token: {num_used}, "
                f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
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                f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
                f"#queue-req: {len(self.waiting_queue)}, "
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            )
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            spec_accept_length = 0
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        else:
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            spec_accept_length = (
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                self.spec_num_total_accepted_tokens / self.spec_num_total_forward_ct
            )
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            self.cum_spec_accept_length += self.spec_num_total_accepted_tokens
            self.cum_spec_accept_count += self.spec_num_total_forward_ct
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            self.spec_num_total_accepted_tokens = self.spec_num_total_forward_ct = 0
            msg = (
                f"Decode batch. "
                f"#running-req: {num_running_reqs}, "
                f"#token: {num_used}, "
                f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
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                f"accept len: {spec_accept_length:.2f}, "
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                f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
                f"#queue-req: {len(self.waiting_queue)}, "
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            )

        logger.info(msg)
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        if self.enable_metrics:
            self.stats.num_running_reqs = num_running_reqs
            self.stats.num_used_tokens = num_used
            self.stats.token_usage = num_used / self.max_total_num_tokens
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            self.stats.cache_hit_rate = 0.0
            self.stats.gen_throughput = self.last_gen_throughput
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            self.stats.num_queue_reqs = len(self.waiting_queue)
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            self.stats.spec_accept_length = spec_accept_length
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            self.metrics_collector.log_stats(self.stats)

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    def check_memory(self):
        available_size = (
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            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
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        )
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        protected_size = self.tree_cache.protected_size()
        memory_leak = available_size != (
            self.max_total_num_tokens
            if not self.enable_hierarchical_cache
            else self.max_total_num_tokens - protected_size
        )
        if memory_leak:
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            msg = (
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                "token_to_kv_pool_allocator memory leak detected! "
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                f"{available_size=}, {protected_size=}, {self.max_total_num_tokens=}\n"
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                f"{self.token_to_kv_pool_allocator.available_size()=}\n"
                f"{self.tree_cache.evictable_size()=}\n"
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            )
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            warnings.warn(msg)
            if crash_on_warnings():
                raise ValueError(msg)
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        if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
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            msg = (
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                "req_to_token_pool memory leak detected!"
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                f"available_size={len(self.req_to_token_pool.free_slots)}, "
                f"total_size={self.req_to_token_pool.size}\n"
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1092
            )
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            warnings.warn(msg)
            if crash_on_warnings():
                raise ValueError(msg)
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        if (
            self.enable_metrics
            and self.attn_tp_rank == 0
            and time.time() > self.metrics_collector.last_log_time + 30
        ):
            # During idle time, also collect metrics every 30 seconds.
            num_used = self.max_total_num_tokens - (
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                self.token_to_kv_pool_allocator.available_size()
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                + self.tree_cache.evictable_size()
            )
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            num_running_reqs = len(self.running_batch.reqs)
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            self.stats.num_running_reqs = num_running_reqs
            self.stats.num_used_tokens = num_used
            self.stats.token_usage = num_used / self.max_total_num_tokens
            self.stats.gen_throughput = 0
            self.stats.num_queue_reqs = len(self.waiting_queue)
            self.metrics_collector.log_stats(self.stats)

1115
    def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
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        # Merge the prefill batch into the running batch
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        if self.last_batch and self.last_batch.forward_mode.is_extend():
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            if self.chunked_req:
                # Move the chunked request out of the batch so that we can merge
                # only finished requests to running_batch.
                self.last_batch.filter_batch(chunked_req_to_exclude=self.chunked_req)
                self.tree_cache.cache_unfinished_req(self.chunked_req)
                # chunked request keeps its rid but will get a new req_pool_idx
                self.req_to_token_pool.free(self.chunked_req.req_pool_idx)
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                self.running_batch.batch_is_full = False
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            # Filter batch
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            last_bs = self.last_batch.batch_size()
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            self.last_batch.filter_batch()
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            if self.last_batch.batch_size() < last_bs:
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                self.running_batch.batch_is_full = False
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            # Merge the new batch into the running batch
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            if not self.last_batch.is_empty():
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                if self.running_batch.is_empty():
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                    self.running_batch = self.last_batch
                else:
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                    # Merge running_batch with prefill batch
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                    self.running_batch.merge_batch(self.last_batch)
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        new_batch = self.get_new_batch_prefill()
        if new_batch is not None:
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            # Run prefill first if possible
            ret = new_batch
        else:
            # Run decode
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            if not self.running_batch.is_empty():
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                self.running_batch = self.update_running_batch(self.running_batch)
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                ret = self.running_batch if not self.running_batch.is_empty() else None
            else:
                ret = None
1152

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        # Handle DP attention
1154
        if self.server_args.enable_dp_attention or self.server_args.enable_sp_layernorm:
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            ret, _ = self.prepare_dp_attn_batch(ret)
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        return ret
1158

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1159
    def get_new_batch_prefill(self) -> Optional[ScheduleBatch]:
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1160
        # Check if the grammar is ready in the grammar queue
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        if self.grammar_queue:
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            self.move_ready_grammar_requests()
1163

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        # Handle the cases where prefill is not allowed
        if (
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            self.running_batch.batch_is_full or len(self.waiting_queue) == 0
1167
        ) and self.chunked_req is None:
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            return None

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        running_bs = len(self.running_batch.reqs)
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        if running_bs >= self.max_running_requests:
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            self.running_batch.batch_is_full = True
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            return None

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        if self.enable_hierarchical_cache:
            # check for completion of hierarchical cache activities to release memory
            self.tree_cache.writing_check()
            self.tree_cache.loading_check()

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        # Get priority queue
        prefix_computed = self.policy.calc_priority(self.waiting_queue)

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        # Prefill policy
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        adder = PrefillAdder(
            self.tree_cache,
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            self.token_to_kv_pool_allocator,
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            self.running_batch,
            self.new_token_ratio,
            self.max_prefill_tokens,
            self.chunked_prefill_size,
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            running_bs if self.is_mixed_chunk else 0,
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        )

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        if self.chunked_req is not None:
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            self.chunked_req.init_next_round_input()
            self.chunked_req = adder.add_chunked_req(self.chunked_req)
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        if self.lora_paths:
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            lora_set = set([req.lora_path for req in self.running_batch.reqs])

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        # Get requests from the waiting queue to a new prefill batch
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        for req in self.waiting_queue:
            if (
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                self.lora_paths
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                and len(
                    lora_set
                    | set([req.lora_path for req in adder.can_run_list])
                    | set([req.lora_path])
                )
                > self.max_loras_per_batch
            ):
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                self.running_batch.batch_is_full = True
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                break

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            if running_bs + len(adder.can_run_list) >= self.max_running_requests:
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                self.running_batch.batch_is_full = True
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                break
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            req.init_next_round_input(
                None if prefix_computed else self.tree_cache,
                self.enable_hierarchical_cache,
            )
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            res = adder.add_one_req(
                req, self.chunked_req, self.enable_hierarchical_cache
            )
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            if res != AddReqResult.CONTINUE:
                if res == AddReqResult.NO_TOKEN:
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                    if self.enable_hierarchical_cache:
                        # Set batch_is_full after making sure there are requests that can be served
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                        self.running_batch.batch_is_full = len(
                            adder.can_run_list
                        ) > 0 or (
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                            self.running_batch is not None
                            and not self.running_batch.is_empty()
                        )
                    else:
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                        self.running_batch.batch_is_full = True
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                break

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        # Update waiting queue
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        can_run_list: List[Req] = adder.can_run_list
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        if len(can_run_list) == 0:
            return None
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        if self.enable_metrics:
            # only record queue time when enable_metrics is True to avoid overhead
            for req in can_run_list:
                req.queue_time_end = time.time()

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        self.waiting_queue = [
            x for x in self.waiting_queue if x not in set(can_run_list)
        ]
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        if self.enable_hierarchical_cache:
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            self.tree_cache.ready_to_load_cache()
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        if adder.new_chunked_req is not None:
            assert self.chunked_req is None
            self.chunked_req = adder.new_chunked_req
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        if self.chunked_req:
            self.chunked_req.is_chunked += 1
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        # Print stats
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        if self.attn_tp_rank == 0:
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            self.log_prefill_stats(adder, can_run_list, running_bs)
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        # Create a new batch
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        new_batch = ScheduleBatch.init_new(
            can_run_list,
            self.req_to_token_pool,
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            self.token_to_kv_pool_allocator,
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            self.tree_cache,
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            self.model_config,
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            self.enable_overlap,
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            self.spec_algorithm,
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            self.server_args.enable_custom_logit_processor,
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        )
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        new_batch.prepare_for_extend()
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        # Mixed-style chunked prefill
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        if (
            self.is_mixed_chunk
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            and not self.running_batch.is_empty()
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            and not (new_batch.return_logprob or self.running_batch.return_logprob)
        ):
            # TODO (lianmin): support return_logprob + mixed chunked prefill
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            self.running_batch.filter_batch()
            if not self.running_batch.is_empty():
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                self.running_batch.prepare_for_decode()
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                new_batch.mix_with_running(self.running_batch)
                new_batch.decoding_reqs = self.running_batch.reqs
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            self.running_batch = ScheduleBatch(
                reqs=[], batch_is_full=self.running_batch.batch_is_full
            )
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        else:
            new_batch.decoding_reqs = None
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        return new_batch

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    def update_running_batch(self, batch: ScheduleBatch) -> Optional[ScheduleBatch]:
1303
        """Update the current running decoding batch."""
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        initial_bs = batch.batch_size()
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        batch.filter_batch()
        if batch.is_empty():
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            batch.batch_is_full = False
            return batch
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        # Check if decode out of memory
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        if not batch.check_decode_mem(self.decode_mem_cache_buf_multiplier) or (
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            TEST_RETRACT and batch.batch_size() > 10
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        ):
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            old_ratio = self.new_token_ratio

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            retracted_reqs, new_token_ratio = batch.retract_decode(self.server_args)
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            self.new_token_ratio = new_token_ratio
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            logger.info(
                "Decode out of memory happened. "
                f"#retracted_reqs: {len(retracted_reqs)}, "
                f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
            )
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            self._extend_requests_to_queue(retracted_reqs)
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        else:
            self.new_token_ratio = max(
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                self.new_token_ratio - self.new_token_ratio_decay,
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                self.min_new_token_ratio,
            )

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        if batch.batch_size() < initial_bs:
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            batch.batch_is_full = False
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        # Update batch tensors
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        batch.prepare_for_decode()
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        return batch
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    def run_batch(
        self, batch: ScheduleBatch
    ) -> Union[GenerationBatchResult, EmbeddingBatchResult]:
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        """Run a batch."""
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        self.forward_ct += 1

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        # Check profiler
        if (
            self.profiler_target_forward_ct
            and self.profiler_target_forward_ct <= self.forward_ct
        ):
            self.stop_profile()

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        # Run forward
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        if self.is_generation:
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            if self.spec_algorithm.is_none():
                model_worker_batch = batch.get_model_worker_batch()
                logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
                    model_worker_batch
                )
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                bid = model_worker_batch.bid
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            else:
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                (
                    logits_output,
                    next_token_ids,
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                    bid,
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                    num_accepted_tokens,
                ) = self.draft_worker.forward_batch_speculative_generation(batch)
                self.spec_num_total_accepted_tokens += (
                    num_accepted_tokens + batch.batch_size()
                )
                self.spec_num_total_forward_ct += batch.batch_size()
                self.num_generated_tokens += num_accepted_tokens
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            batch.output_ids = next_token_ids
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            # These 2 values are needed for processing the output, but the values can be
            # modified by overlap schedule. So we have to copy them here so that
            # we can use the correct values in output processing.
            if batch.return_logprob:
                extend_input_len_per_req = [req.extend_input_len for req in batch.reqs]
                extend_logprob_start_len_per_req = [
                    req.extend_logprob_start_len for req in batch.reqs
                ]
            else:
                extend_input_len_per_req = None
                extend_logprob_start_len_per_req = None

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            ret = GenerationBatchResult(
                logits_output=logits_output,
                next_token_ids=next_token_ids,
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                extend_input_len_per_req=extend_input_len_per_req,
                extend_logprob_start_len_per_req=extend_logprob_start_len_per_req,
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                bid=bid,
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            )
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        else:  # embedding or reward model
            model_worker_batch = batch.get_model_worker_batch()
            embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
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            ret = EmbeddingBatchResult(
                embeddings=embeddings, bid=model_worker_batch.bid
            )
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        return ret
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    def process_batch_result(
        self,
        batch: ScheduleBatch,
        result: Union[GenerationBatchResult, EmbeddingBatchResult],
    ):
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        if batch.forward_mode.is_decode():
            self.process_batch_result_decode(batch, result)
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        elif batch.forward_mode.is_extend():
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            self.process_batch_result_prefill(batch, result)
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        elif batch.forward_mode.is_idle():
            if self.enable_overlap:
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                self.tp_worker.resolve_batch_result(result.bid)
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                if batch.next_batch_sampling_info:
                    batch.next_batch_sampling_info.update_regex_vocab_mask()
                    self.current_stream.synchronize()
                    batch.next_batch_sampling_info.sampling_info_done.set()
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        elif batch.forward_mode.is_dummy_first():
            batch.next_batch_sampling_info.update_regex_vocab_mask()
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            self.current_stream.synchronize()
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            batch.next_batch_sampling_info.sampling_info_done.set()
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        if self.return_health_check_ct:
            # Return some signal for the health check.
            # This is used to prevent the health check signal being blocked by long context prefill.
            # However, one minor issue is that this code path does not check the status of detokenizer manager.
            self.return_health_check_ct -= 1
            self.send_to_tokenizer.send_pyobj(HealthCheckOutput())

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    def prepare_dp_attn_batch(self, local_batch: ScheduleBatch):
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        return self.prepare_dp_attn_batch_raw(
            local_batch,
            dp_size=self.server_args.dp_size,
            attn_tp_size=self.attn_tp_size,
            tp_cpu_group=self.tp_cpu_group,
            get_idle_batch=self.get_idle_batch,
            disable_cuda_graph=self.server_args.disable_cuda_graph,
            spec_algorithm=self.spec_algorithm,
            speculative_num_draft_tokens=self.server_args.speculative_num_draft_tokens,
        )

    @staticmethod
    def prepare_dp_attn_batch_raw(
        local_batch: ScheduleBatch,
        dp_size,
        attn_tp_size: int,
        tp_cpu_group,
        get_idle_batch,
        disable_cuda_graph: bool,
        spec_algorithm,
        speculative_num_draft_tokens,
    ):
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        # Check if other DP workers have running batches
        if local_batch is None:
            num_tokens = 0
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            global_num_tokens_for_logprob = 0
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        elif local_batch.forward_mode.is_decode():
            num_tokens = local_batch.batch_size()
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            if not spec_algorithm.is_none() and spec_algorithm.is_eagle():
                num_tokens = num_tokens * speculative_num_draft_tokens
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            global_num_tokens_for_logprob = num_tokens
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        else:
            num_tokens = local_batch.extend_num_tokens
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            global_num_tokens_for_logprob = sum(
                [
                    # We should have at least 1 token for sample in every case.
                    max(extend_len - logprob_start_len, 1)
                    for logprob_start_len, extend_len in zip(
                        local_batch.extend_logprob_start_lens, local_batch.extend_lens
                    )
                ]
            )

        if local_batch is None or local_batch.forward_mode.is_decode_or_idle():
            can_cuda_graph = 1
        else:
            can_cuda_graph = 0

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        if not spec_algorithm.is_none():
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            # TODO(sang): Support cuda graph when idle batch is there.
            if local_batch is None or local_batch.forward_mode.is_idle():
                can_cuda_graph = 0
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        is_extend_in_batch = (
            local_batch.forward_mode.is_extend() if local_batch else False
        )
        local_info = torch.tensor(
            [
                num_tokens,
                can_cuda_graph,
                global_num_tokens_for_logprob,
                is_extend_in_batch,
            ],
            dtype=torch.int64,
        )
        global_info = torch.empty(
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            (dp_size, attn_tp_size, 4),
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            dtype=torch.int64,
        )
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        torch.distributed.all_gather_into_tensor(
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            global_info.flatten(),
            local_info,
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            group=tp_cpu_group,
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        )
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        global_num_tokens = global_info[:, 0, 0].tolist()
        can_cuda_graph = min(global_info[:, 0, 1].tolist())
        global_num_tokens_for_logprob = global_info[:, 0, 2].tolist()
        is_extend_in_batch = global_info[:, 0, 3].tolist()
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        if local_batch is None and max(global_num_tokens) > 0:
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            local_batch = get_idle_batch()
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        if local_batch is not None:
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            local_batch.global_num_tokens = global_num_tokens
            local_batch.global_num_tokens_for_logprob = global_num_tokens_for_logprob
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            # Check forward mode for cuda graph
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            if not disable_cuda_graph:
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                local_batch.can_run_dp_cuda_graph = can_cuda_graph
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        return local_batch, any(is_extend_in_batch)
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    def get_idle_batch(self):
        idle_batch = ScheduleBatch.init_new(
            [],
            self.req_to_token_pool,
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            self.token_to_kv_pool_allocator,
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            self.tree_cache,
            self.model_config,
            self.enable_overlap,
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            self.spec_algorithm,
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            self.server_args.enable_custom_logit_processor,
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        )
        idle_batch.prepare_for_idle()
        return idle_batch

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    def move_ready_grammar_requests(self):
        """Move requests whose grammar objects are ready from grammar_queue to waiting_queue."""
        num_ready_reqs = 0
        for req in self.grammar_queue:
            try:
                req.grammar = req.grammar.result(timeout=0.05)
                num_ready_reqs += 1
            except futures._base.TimeoutError:
                break

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        if self.server_args.enable_dp_attention:
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            tp_size = self.attn_tp_size
            tp_group = self.attn_tp_cpu_group
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        else:
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            tp_size = self.tp_size
            tp_group = self.tp_cpu_group

        if tp_size > 1:
            # Sync across TP ranks to make sure they have the same number of ready requests
            tensor = torch.tensor(num_ready_reqs, dtype=torch.int32)
            torch.distributed.all_reduce(
                tensor, op=torch.distributed.ReduceOp.MAX, group=tp_group
            )
            num_ready_reqs_max = tensor.item()
            for i in range(num_ready_reqs, num_ready_reqs_max):
                self.grammar_queue[i].grammar = self.grammar_queue[i].grammar.result()
            num_ready_reqs = num_ready_reqs_max
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        self._extend_requests_to_queue(self.grammar_queue[:num_ready_reqs])
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        self.grammar_queue = self.grammar_queue[num_ready_reqs:]

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    def watchdog_thread(self):
        """A watch dog thread that will try to kill the server itself if one forward batch takes too long."""
        self.watchdog_last_forward_ct = 0
        self.watchdog_last_time = time.time()

        while True:
            current = time.time()
            if self.cur_batch is not None:
                if self.watchdog_last_forward_ct == self.forward_ct:
                    if current > self.watchdog_last_time + self.watchdog_timeout:
                        logger.error(f"Watchdog timeout ({self.watchdog_timeout=})")
                        break
                else:
                    self.watchdog_last_forward_ct = self.forward_ct
                    self.watchdog_last_time = current
            time.sleep(self.watchdog_timeout // 2)

        # Print batch size and memory pool info to check whether there are de-sync issues.
        logger.error(
            f"{self.cur_batch.batch_size()=}, "
            f"{self.cur_batch.reqs=}, "
            f"{self.token_to_kv_pool_allocator.available_size()=}, "
            f"{self.tree_cache.evictable_size()=}, "
        )
        # Wait for some time so that the parent process can print the error.
        pyspy_dump_schedulers()
        print(file=sys.stderr, flush=True)
        print(file=sys.stdout, flush=True)
        time.sleep(5)
        self.parent_process.send_signal(signal.SIGQUIT)

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    def flush_cache_wrapped(self, recv_req: FlushCacheReq):
        self.flush_cache()

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    def flush_cache(self):
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        """Flush the memory pool and cache."""
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        if len(self.waiting_queue) == 0 and self.running_batch.is_empty():
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            self.cur_batch = None
            self.last_batch = None
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            self.tree_cache.reset()
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            if self.grammar_backend:
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                self.grammar_backend.reset()
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            self.req_to_token_pool.clear()
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            self.token_to_kv_pool_allocator.clear()
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            if not self.spec_algorithm.is_none():
                self.draft_worker.model_runner.req_to_token_pool.clear()
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                self.draft_worker.model_runner.token_to_kv_pool_allocator.clear()
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            self.num_generated_tokens = 0
            self.forward_ct_decode = 0
            self.spec_num_total_accepted_tokens = 0
            self.spec_num_total_forward_ct = 0
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            self.cum_spec_accept_length = 0
            self.cum_spec_accept_count = 0
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            torch.cuda.empty_cache()
            logger.info("Cache flushed successfully!")
            if_success = True
        else:
            logging.warning(
                f"Cache not flushed because there are pending requests. "
                f"#queue-req: {len(self.waiting_queue)}, "
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                f"#running-req: {len(self.running_batch.reqs)}"
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            )
            if_success = False
        return if_success

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    def get_internal_state(self, recv_req: GetInternalStateReq):
        ret = dict(global_server_args_dict)
        ret["last_gen_throughput"] = self.last_gen_throughput
        if not self.spec_algorithm.is_none() and self.cum_spec_accept_count > 0:
            ret["avg_spec_accept_length"] = (
                self.cum_spec_accept_length / self.cum_spec_accept_count
            )

        if RECORD_STEP_TIME:
            ret["step_time_dict"] = self.step_time_dict
        return GetInternalStateReqOutput(
            internal_state=ret,
        )

    def set_internal_state(self, recv_req: SetInternalStateReq):
        server_args_dict = recv_req.server_args
        args_allow_update = set(
            [
                "speculative_accept_threshold_single",
                "speculative_accept_threshold_acc",
            ]
        )
        if_success = True
        for k, v in server_args_dict.items():
            if k not in args_allow_update:
                logging.warning(f"Updating {k} is not supported.")
                if_success = False
                break
        if if_success:
            if not self.spec_algorithm.is_none() and self.cum_spec_accept_count > 0:
                avg_spec_accept_length = (
                    self.cum_spec_accept_length / self.cum_spec_accept_count
                )
                logger.info(f"{avg_spec_accept_length=}")
            self.cum_spec_accept_length = self.cum_spec_accept_count = 0
            for k, v in server_args_dict.items():
                global_server_args_dict[k] = v
            logger.info(f"Global server args updated! " f"{global_server_args_dict=}")
        return SetInternalStateReqOutput(
            updated=True,
            server_args=global_server_args_dict,
        )

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    def handle_rpc_request(self, recv_req: RpcReqInput):
        # Handle RPC requests
        logger.info(
            f"handle_rpc_request: {recv_req.method}, param: {recv_req.parameters}"
        )

        success = True
        exec = None
        try:
            func = getattr(self, recv_req.method)
            func(recv_req.parameters)
        except Exception as e:
            success = False
            exec = e
            logger.error(f"Failed to call rpc {recv_req.method}: {str(e)}")

        barrier()
        return RpcReqOutput(success, "" if not exec else str(exec))

    def save_remote_model(self, params):
        url = params["url"]

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        worker = self.tp_worker.worker
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        worker.model_runner.save_remote_model(url)

    def save_sharded_model(self, params):
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        worker = self.tp_worker.worker
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        worker.model_runner.save_sharded_model(
            path=params["path"],
            pattern=params["pattern"],
            max_size=params["max_size"],
        )

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    def abort_request(self, recv_req: AbortReq):
        # Delete requests in the waiting queue
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        to_del = []
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        for i, req in enumerate(self.waiting_queue):
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            if req.rid.startswith(recv_req.rid):
                to_del.append(i)
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                break

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        # Sort in reverse order to avoid index issues when deleting
        for i in sorted(to_del, reverse=True):
            req = self.waiting_queue.pop(i)
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            logger.debug(f"Abort queued request. {req.rid=}")
            return
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        # Delete requests in the running batch
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        for req in self.running_batch.reqs:
            if req.rid.startswith(recv_req.rid) and not req.finished():
                logger.debug(f"Abort running request. {req.rid=}")
                req.to_abort = True
                return
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    def _pause_engine(self) -> Tuple[List[Req], int]:
        raise NotImplementedError()

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    def update_weights_from_disk(self, recv_req: UpdateWeightFromDiskReqInput):
        """In-place update of the weights from disk."""
        success, message = self.tp_worker.update_weights_from_disk(recv_req)
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        if success:
            flash_cache_success = self.flush_cache()
            assert flash_cache_success, "Cache flush failed after updating weights"
        else:
            logger.error(message)
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        return UpdateWeightFromDiskReqOutput(success, message, 0)
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    def init_weights_update_group(self, recv_req: InitWeightsUpdateGroupReqInput):
        """Initialize the online model parameter update group."""
        success, message = self.tp_worker.init_weights_update_group(recv_req)
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        return InitWeightsUpdateGroupReqOutput(success, message)
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    def update_weights_from_distributed(
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        self,
        recv_req: UpdateWeightsFromDistributedReqInput,
    ) -> Tuple[bool, str]:
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        """Update the online model parameter."""
        success, message = self.tp_worker.update_weights_from_distributed(recv_req)
        if success:
            flash_cache_success = self.flush_cache()
            assert flash_cache_success, "Cache flush failed after updating weights"
        else:
            logger.error(message)
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        return UpdateWeightsFromDistributedReqOutput(success, message)
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    def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
        """Update the online model parameter from tensors."""
        success, message = self.tp_worker.update_weights_from_tensor(recv_req)
        # TODO extract common code b/t update_weights_from_distributed and update_weights_from_tensor later
        if success:
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            if recv_req.flush_cache:
                flash_cache_success = self.flush_cache()
                assert flash_cache_success, "Cache flush failed after updating weights"
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        else:
            logger.error(message)
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        return UpdateWeightsFromTensorReqOutput(success, message)
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    def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
        parameter = self.tp_worker.get_weights_by_name(recv_req)
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        return GetWeightsByNameReqOutput(parameter)
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    def release_memory_occupation(self, recv_req: ReleaseMemoryOccupationReqInput):
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        self.memory_saver_adapter.check_validity(
            caller_name="release_memory_occupation"
        )
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        self.stashed_model_static_state = _export_static_state(
            self.tp_worker.worker.model_runner.model
        )
        self.memory_saver_adapter.pause()
        self.flush_cache()
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        return ReleaseMemoryOccupationReqOutput()
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    def resume_memory_occupation(self, recv_req: ResumeMemoryOccupationReqInput):
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        self.memory_saver_adapter.check_validity(caller_name="resume_memory_occupation")
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        self.memory_saver_adapter.resume()
        _import_static_state(
            self.tp_worker.worker.model_runner.model, self.stashed_model_static_state
        )
        del self.stashed_model_static_state
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        return ResumeMemoryOccupationReqOutput()

    def profile(self, recv_req: ProfileReq):
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        if recv_req.type == ProfileReqType.START_PROFILE:
            return self.start_profile(
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                recv_req.output_dir,
                recv_req.num_steps,
                recv_req.activities,
                recv_req.with_stack,
                recv_req.record_shapes,
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            )
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        else:
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            return self.stop_profile()

    def start_profile(
        self,
        output_dir: Optional[str],
        num_steps: Optional[int],
        activities: Optional[List[str]],
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        with_stack: Optional[bool],
        record_shapes: Optional[bool],
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    ) -> None:
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        if self.profiler_activities:
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            return ProfileReqOutput(
                success=False,
                message="Profiling is already in progress. Call /stop_profile first.",
            )

        if output_dir is None:
            output_dir = os.getenv("SGLANG_TORCH_PROFILER_DIR", "/tmp")
        if activities is None:
            activities = ["CPU", "GPU"]

        self.torch_profiler_output_dir = output_dir
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        self.profiler_activities = activities
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        logger.info(
            "Profiling starts. Traces will be saved to: %s",
            self.torch_profiler_output_dir,
        )

        activity_map = {
            "CPU": torch.profiler.ProfilerActivity.CPU,
            "GPU": torch.profiler.ProfilerActivity.CUDA,
        }
        torchprof_activities = [
            activity_map[a] for a in activities if a in activity_map
        ]

        if torchprof_activities:
            self.torch_profiler = torch.profiler.profile(
                activities=torchprof_activities,
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                with_stack=with_stack if with_stack is not None else True,
                record_shapes=record_shapes if record_shapes is not None else False,
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            )
            self.torch_profiler.start()

        if "MEM" in activities:
            torch.cuda.memory._record_memory_history(max_entries=100000)
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        if "CUDA_PROFILER" in activities:
            torch.cuda.cudart().cudaProfilerStart()

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        if num_steps:
            self.profiler_target_forward_ct = self.forward_ct + num_steps
            # The caller will be notified when reaching profiler_target_forward_ct
        else:
            self.profiler_target_forward_ct = None
            return ProfileReqOutput(success=True, message="Succeeded")
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    def stop_profile(self) -> None:
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        if self.profiler_activities is None:
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            return

        logger.info("Stop profiling...")
        if self.torch_profiler is not None:
            self.torch_profiler.stop()
            self.torch_profiler.export_chrome_trace(
                os.path.join(
                    self.torch_profiler_output_dir,
                    str(time.time()) + f"-TP-{self.tp_rank}" + ".trace.json.gz",
                )
            )

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            memory_profile_path = os.path.join(
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                self.torch_profiler_output_dir,
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                str(time.time()) + f"-TP-{self.tp_rank}-memory" + ".pickle",
            )
            torch.cuda.memory._dump_snapshot(memory_profile_path)
            torch.cuda.memory._record_memory_history(enabled=None)

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        if "CUDA_PROFILER" in self.profiler_activities:
            torch.cuda.cudart().cudaProfilerStop()

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        logger.info(
            "Profiling done. Traces are saved to: %s",
            self.torch_profiler_output_dir,
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        )
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        self.torch_profiler_output_dir = None
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        self.profiler_activities = None
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        if self.profiler_target_forward_ct:
            self.send_to_tokenizer.send_pyobj(
                ProfileReqOutput(success=True, message="Succeeded.")
            )
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    def expert_distribution_handle(self, recv_req: ExpertDistributionReq):
        if recv_req == ExpertDistributionReq.START_RECORD:
            expert_distribution_recorder.start_record()
        elif recv_req == ExpertDistributionReq.STOP_RECORD:
            expert_distribution_recorder.stop_record()
        elif recv_req == ExpertDistributionReq.DUMP_RECORD:
            expert_distribution_recorder.dump_record()
        else:
            raise ValueError("Unrecognized ExpertDistributionReq value")
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        return ExpertDistributionReqOutput()
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    def open_session(self, recv_req: OpenSessionReqInput):
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        # handle error
        session_id = recv_req.session_id
        if session_id in self.sessions:
            logger.warning(f"session id {session_id} already exist, cannot open.")
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            return OpenSessionReqOutput(session_id, False)
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        elif session_id is None:
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            logger.warning("session id is None, cannot open.")
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            return OpenSessionReqOutput(session_id, False)
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        else:
            self.sessions[session_id] = Session(
                recv_req.capacity_of_str_len, session_id
            )
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            return OpenSessionReqOutput(session_id, True)
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    def close_session(self, recv_req: CloseSessionReqInput):
        # handle error
        session_id = recv_req.session_id
        if session_id not in self.sessions:
            logger.warning(f"session id {session_id} does not exist, cannot delete.")
        else:
            del self.sessions[session_id]

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def is_health_check_generate_req(recv_req):
    return getattr(recv_req, "rid", "").startswith("HEALTH_CHECK")


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def _export_static_state(model):
    return dict(
        buffers=[
            (name, buffer.detach().clone()) for name, buffer in model.named_buffers()
        ]
    )


def _import_static_state(model, static_params):
    self_named_buffers = dict(model.named_buffers())
    for name, tensor in static_params["buffers"]:
        self_named_buffers[name][...] = tensor


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def run_scheduler_process(
    server_args: ServerArgs,
    port_args: PortArgs,
    gpu_id: int,
    tp_rank: int,
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    dp_rank: Optional[int],
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    pipe_writer,
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):
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    # Generate the prefix
    if dp_rank is None:
        prefix = f" TP{tp_rank}"
    else:
        prefix = f" DP{dp_rank} TP{tp_rank}"

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    # Config the process
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    kill_itself_when_parent_died()
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    setproctitle.setproctitle(f"sglang::scheduler{prefix.replace(' ', '_')}")
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    faulthandler.enable()
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    parent_process = psutil.Process().parent()
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    # [For Router] if env var "SGLANG_DP_RANK" exist, set dp_rank to the value of the env var
    if dp_rank is None and "SGLANG_DP_RANK" in os.environ:
        dp_rank = int(os.environ["SGLANG_DP_RANK"])
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    # Configure the logger
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    configure_logger(server_args, prefix=prefix)
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    suppress_other_loggers()
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    # Set cpu affinity to this gpu process
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    if get_bool_env_var("SGLANG_SET_CPU_AFFINITY"):
        set_gpu_proc_affinity(server_args.tp_size, server_args.nnodes, gpu_id)

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    # Create a scheduler and run the event loop
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    try:
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        scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank)
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        pipe_writer.send(
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            {
                "status": "ready",
                "max_total_num_tokens": scheduler.max_total_num_tokens,
                "max_req_input_len": scheduler.max_req_input_len,
            }
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        )
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        disaggregation_mode: DisaggregationMode = scheduler.disaggregation_mode

        if disaggregation_mode == DisaggregationMode.NULL:
            if scheduler.enable_overlap:
                scheduler.event_loop_overlap()
            else:
                scheduler.event_loop_normal()
        elif disaggregation_mode == DisaggregationMode.PREFILL:
            scheduler.event_loop_normal_disagg_prefill()
        elif disaggregation_mode == DisaggregationMode.DECODE:
            scheduler.event_loop_normal_disagg_decode()

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    except Exception:
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        traceback = get_exception_traceback()
        logger.error(f"Scheduler hit an exception: {traceback}")
        parent_process.send_signal(signal.SIGQUIT)