scheduler.py

# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A scheduler that manages a tensor parallel GPU worker."""

import faulthandler
import logging
import os
import signal
import sys
import threading
import time
import warnings
from collections import defaultdict, deque
from concurrent import futures
from dataclasses import dataclass
from http import HTTPStatus
from types import SimpleNamespace
from typing import Dict, List, Optional, Tuple, Union

import psutil
import setproctitle
import torch
import zmq

from sglang.global_config import global_config
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.constrained.base_grammar_backend import create_grammar_backend
from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.managers.io_struct import (
    AbortReq,
    BatchEmbeddingOut,
    BatchTokenIDOut,
    CloseSessionReqInput,
    FlushCacheReq,
    GetInternalStateReq,
    GetInternalStateReqOutput,
    GetWeightsByNameReqInput,
    GetWeightsByNameReqOutput,
    HealthCheckOutput,
    InitWeightsUpdateGroupReqInput,
    InitWeightsUpdateGroupReqOutput,
    OpenSessionReqInput,
    OpenSessionReqOutput,
    ProfileReq,
    ProfileReqOutput,
    ProfileReqType,
    ReleaseMemoryOccupationReqInput,
    ReleaseMemoryOccupationReqOutput,
    ResumeMemoryOccupationReqInput,
    ResumeMemoryOccupationReqOutput,
    SetInternalStateReq,
    SetInternalStateReqOutput,
    TokenizedEmbeddingReqInput,
    TokenizedGenerateReqInput,
    UpdateWeightFromDiskReqInput,
    UpdateWeightFromDiskReqOutput,
    UpdateWeightsFromDistributedReqInput,
    UpdateWeightsFromDistributedReqOutput,
    UpdateWeightsFromTensorReqInput,
    UpdateWeightsFromTensorReqOutput,
)
from sglang.srt.managers.schedule_batch import (
    FINISH_ABORT,
    BaseFinishReason,
    ImageInputs,
    Req,
    ScheduleBatch,
    global_server_args_dict,
)
from sglang.srt.managers.schedule_policy import (
    AddReqResult,
    PrefillAdder,
    SchedulePolicy,
)
from sglang.srt.managers.session_controller import Session
from sglang.srt.managers.tp_worker import TpModelWorker
from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient
from sglang.srt.managers.utils import validate_input_length
from sglang.srt.mem_cache.chunk_cache import ChunkCache
from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
from sglang.srt.utils import (
    broadcast_pyobj,
    configure_logger,
    crash_on_warnings,
    get_bool_env_var,
    get_zmq_socket,
    pyspy_dump_schedulers,
    set_gpu_proc_affinity,
    set_random_seed,
    suppress_other_loggers,
)
from sglang.utils import TypeBasedDispatcher, get_exception_traceback

logger = logging.getLogger(__name__)

# Test retract decode for debugging purposes
TEST_RETRACT = get_bool_env_var("SGLANG_TEST_RETRACT")
RECORD_STEP_TIME = get_bool_env_var("SGLANG_RECORD_STEP_TIME")


@dataclass
class GenerationBatchResult:
    logits_output: LogitsProcessorOutput
    next_token_ids: List[int]
    extend_input_len_per_req: List[int]
    extend_logprob_start_len_per_req: List[int]
    bid: int


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


class Scheduler:
    """A scheduler that manages a tensor parallel GPU worker."""

    def __init__(
        self,
        server_args: ServerArgs,
        port_args: PortArgs,
        gpu_id: int,
        tp_rank: int,
        dp_rank: Optional[int],
    ):
        # Parse args
        self.server_args = server_args
        self.tp_rank = tp_rank
        self.tp_size = server_args.tp_size
        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
        self.enable_overlap = not server_args.disable_overlap_schedule
        self.skip_tokenizer_init = server_args.skip_tokenizer_init
        self.enable_metrics = server_args.enable_metrics
        self.stream_interval = server_args.stream_interval
        self.spec_algorithm = SpeculativeAlgorithm.from_string(
            server_args.speculative_algorithm
        )
        self.gpu_id = gpu_id
        self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
        self.decode_mem_cache_buf_multiplier = (
            (
                self.server_args.speculative_num_draft_tokens
                + (
                    self.server_args.speculative_eagle_topk
                    * self.server_args.speculative_num_draft_tokens
                )
            )
            if not self.spec_algorithm.is_none()
            else 1
        )

        # Distributed rank info
        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,
            )
        )

        # Init inter-process communication
        context = zmq.Context(2)
        if self.attn_tp_rank == 0:
            self.recv_from_tokenizer = get_zmq_socket(
                context, zmq.PULL, port_args.scheduler_input_ipc_name, False
            )
            self.send_to_tokenizer = get_zmq_socket(
                context, zmq.PUSH, port_args.tokenizer_ipc_name, False
            )

            if server_args.skip_tokenizer_init:
                # Directly send to the TokenizerManager
                self.send_to_detokenizer = get_zmq_socket(
                    context, zmq.PUSH, port_args.tokenizer_ipc_name, False
                )
            else:
                # Send to the DetokenizerManager
                self.send_to_detokenizer = get_zmq_socket(
                    context, zmq.PUSH, port_args.detokenizer_ipc_name, False
                )
        else:
            self.recv_from_tokenizer = None
            self.send_to_tokenizer = SimpleNamespace(send_pyobj=lambda x: None)
            self.send_to_detokenizer = SimpleNamespace(send_pyobj=lambda x: None)

        # Init tokenizer
        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,
            dtype=server_args.dtype,
            quantization=server_args.quantization,
        )
        self.is_generation = self.model_config.is_generation

        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,
                )
                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,
                )

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

        if self.model_config.is_multimodal:
            self.enable_overlap = False
            logger.info("Overlap scheduler is disabled for multimodal models.")

        # Launch a tensor parallel worker
        if self.enable_overlap:
            TpWorkerClass = TpModelWorkerClient
        else:
            TpWorkerClass = TpModelWorker

        self.tp_worker = TpWorkerClass(
            server_args=server_args,
            gpu_id=gpu_id,
            tp_rank=tp_rank,
            dp_rank=dp_rank,
            nccl_port=port_args.nccl_port,
        )

        # Launch a draft worker for speculative decoding
        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,
            )
            self.prefill_only_one_req = True
        else:
            self.draft_worker = None
            self.prefill_only_one_req = False

        # Get token and memory info from the model worker
        (
            self.max_total_num_tokens,
            self.max_prefill_tokens,
            self.max_running_requests,
            self.max_req_len,
            self.max_req_input_len,
            self.random_seed,
            self.device,
            worker_global_server_args_dict,
            _,
            _,
            _,
        ) = self.tp_worker.get_worker_info()
        self.tp_cpu_group = self.tp_worker.get_tp_cpu_group()
        self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group()
        self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
        global_server_args_dict.update(worker_global_server_args_dict)
        set_random_seed(self.random_seed)

        # Print debug info
        logger.info(
            f"max_total_num_tokens={self.max_total_num_tokens}, "
            f"chunked_prefill_size={server_args.chunked_prefill_size}, "
            f"max_prefill_tokens={self.max_prefill_tokens}, "
            f"max_running_requests={self.max_running_requests}, "
            f"context_len={self.model_config.context_len}"
        )

        # Init memory pool and cache
        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,
                )
            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,
                    disable=server_args.disable_radix_cache,
                )

        self.policy = SchedulePolicy(self.schedule_policy, self.tree_cache)

        # Init running status
        self.waiting_queue: List[Req] = []
        self.staging_reqs = {}
        # The running decoding batch for continuous batching
        self.running_batch: Optional[ScheduleBatch] = None
        # The current forward batch
        self.cur_batch: Optional[ScheduleBatch] = None
        # The current forward batch
        self.last_batch: Optional[ScheduleBatch] = None
        self.forward_ct = 0
        self.forward_ct_decode = 0
        self.num_generated_tokens = 0
        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.last_decode_stats_tic = time.time()
        self.return_health_check_ct = 0
        self.current_stream = torch.get_device_module(self.device).current_stream()
        if self.device == "cpu":
            self.current_stream.synchronize = lambda: None  # No-op for CPU

        # For metrics only.
        # 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
        self.step_time_dict = defaultdict(list)  # Dict[batch size -> step time]

        # Session info
        self.sessions: Dict[str, Session] = {}

        # Init chunked prefill
        self.chunked_prefill_size = server_args.chunked_prefill_size
        if self.chunked_prefill_size <= 0:  # -1 means disable
            self.chunked_prefill_size = None
        self.chunked_req = None
        self.is_mixed_chunk = (
            self.chunked_prefill_size is not None and server_args.enable_mixed_chunk
        )

        # Init the grammar backend for constrained generation
        self.grammar_queue: List[Req] = []
        if not server_args.skip_tokenizer_init:
            self.grammar_backend = create_grammar_backend(
                server_args, self.tokenizer, self.model_config.vocab_size
            )
        else:
            self.grammar_backend = None

        # Init new token estimation
        assert (
            server_args.schedule_conservativeness >= 0
        ), "Invalid schedule_conservativeness"

        self.init_new_token_ratio = min(
            global_config.default_init_new_token_ratio
            * server_args.schedule_conservativeness,
            1.0,
        )
        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

        # Tell whether the current running batch is full so that we can skip
        # the check of whether to prefill new requests.
        # This is an optimization to reduce the overhead of the prefill check.
        self.batch_is_full = False

        # Init watchdog thread
        self.watchdog_timeout = server_args.watchdog_timeout
        t = threading.Thread(target=self.watchdog_thread, daemon=True)
        t.start()
        self.parent_process = psutil.Process().parent()

        # Init memory saver
        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
            enable=server_args.enable_memory_saver
        )

        # Init profiler
        self.torch_profiler = None
        self.torch_profiler_output_dir: Optional[str] = None
        self.torch_profiler_activities: Optional[List[str]] = None
        self.profiler_target_forward_ct: Optional[int] = None

        # Init metrics stats
        self.stats = SchedulerStats()
        if self.enable_metrics:
            self.metrics_collector = SchedulerMetricsCollector(
                labels={
                    "model_name": self.server_args.served_model_name,
                    # TODO: Add lora name/path in the future,
                },
            )

        # Init request dispatcher
        self._request_dispatcher = TypeBasedDispatcher(
            [
                (TokenizedGenerateReqInput, self.handle_generate_request),
                (TokenizedEmbeddingReqInput, self.handle_embedding_request),
                (FlushCacheReq, self.flush_cache_wrapped),
                (AbortReq, self.abort_request),
                (OpenSessionReqInput, self.open_session),
                (CloseSessionReqInput, self.close_session),
                (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),
                (ReleaseMemoryOccupationReqInput, self.release_memory_occupation),
                (ResumeMemoryOccupationReqInput, self.resume_memory_occupation),
                (ProfileReq, self.profile),
                (GetInternalStateReq, self.get_internal_state),
            ]
        )

    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.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)

    @torch.no_grad()
    def event_loop_normal(self):
        """A normal scheduler loop."""
        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)

            batch = self.get_next_batch_to_run()
            self.cur_batch = batch

            if batch:
                result = self.run_batch(batch)
                self.process_batch_result(batch, result)
            else:
                # When the server is idle, so self-check and re-init some states
                self.check_memory()
                self.new_token_ratio = self.init_new_token_ratio

            self.last_batch = batch

    @torch.no_grad()
    def event_loop_overlap(self):
        """A scheduler loop that overlaps the CPU processing and GPU computation."""
        self.result_queue = deque()

        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)

            batch = self.get_next_batch_to_run()
            self.cur_batch = batch

            if batch:
                result = self.run_batch(batch)
                self.result_queue.append((batch.copy(), result))

                if self.last_batch is None:
                    # Create a dummy first batch to start the pipeline for overlap schedule.
                    # 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)

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

            self.last_batch = batch

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

            while True:
                try:
                    recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
                except zmq.ZMQError:
                    break
                recv_reqs.append(recv_req)
        else:
            recv_reqs = None

        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:
            recv_reqs = broadcast_pyobj(recv_reqs, self.tp_rank, self.tp_cpu_group)
        return recv_reqs

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

            output = self._request_dispatcher(recv_req)
            if output is not None:
                self.send_to_tokenizer.send_pyobj(output)

    def handle_generate_request(
        self,
        recv_req: TokenizedGenerateReqInput,
    ):
        # Create a new request
        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
        ):
            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

            # 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

            req = Req(
                recv_req.rid,
                recv_req.input_text,
                recv_req.input_ids,
                recv_req.sampling_params,
                return_logprob=recv_req.return_logprob,
                top_logprobs_num=recv_req.top_logprobs_num,
                token_ids_logprob=recv_req.token_ids_logprob,
                stream=recv_req.stream,
                lora_path=recv_req.lora_path,
                input_embeds=recv_req.input_embeds,
                custom_logit_processor=custom_logit_processor,
                return_hidden_states=recv_req.return_hidden_states,
                eos_token_ids=self.model_config.hf_eos_token_id,
            )
            req.tokenizer = self.tokenizer

            if (
                recv_req.session_params is not None
                and recv_req.session_params.id is not None
            ):
                req.finished_reason = FINISH_ABORT(
                    f"Invalid request: session id {recv_req.session_params.id} does not exist"
                )
                self._add_request_to_queue(req)
                return
        else:
            # Create a new request from a previous session
            session = self.sessions[recv_req.session_params.id]
            req = session.create_req(recv_req, self.tokenizer)
            if isinstance(req.finished_reason, FINISH_ABORT):
                self._add_request_to_queue(req)
                return

        # Handle multimodal inputs
        if recv_req.image_inputs is not None:
            image_inputs = ImageInputs.from_dict(recv_req.image_inputs)
            # 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]
                req.image_inputs = None
                req.sampling_params.max_new_tokens = 0
                req.finished_reason = FINISH_ABORT(
                    error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
                )
                self._add_request_to_queue(req)
                return

        # Validate prompts length
        error_msg = validate_input_length(
            req,
            self.max_req_input_len,
            self.server_args.allow_auto_truncate,
        )
        if error_msg:
            req.origin_input_ids = [0]
            req.sampling_params.max_new_tokens = 0
            self._add_request_to_queue(req)
            return

        # Copy more attributes
        if recv_req.logprob_start_len == -1 or not recv_req.return_logprob:
            # 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

        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

        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
            ),
            self.max_req_len - len(req.origin_input_ids) - 1,
        )

        # 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
            or req.sampling_params.ebnf is not None
            or req.sampling_params.structural_tag is not None
        ):
            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)
            elif req.sampling_params.ebnf is not None:
                key = ("ebnf", req.sampling_params.ebnf)
            elif req.sampling_params.structural_tag:
                key = ("structural_tag", req.sampling_params.structural_tag)

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

    def _add_request_to_queue(self, req: Req):
        self.waiting_queue.append(req)

    def _extend_requests_to_queue(self, reqs: List[Req]):
        self.waiting_queue.extend(reqs)

    def handle_embedding_request(
        self,
        recv_req: TokenizedEmbeddingReqInput,
    ):
        req = Req(
            recv_req.rid,
            recv_req.input_text,
            recv_req.input_ids,
            recv_req.sampling_params,
        )
        req.tokenizer = self.tokenizer

        # Validate prompts length
        error_msg = validate_input_length(
            req,
            self.max_req_input_len,
            self.server_args.allow_auto_truncate,
        )
        if error_msg:
            self._add_request_to_queue(req)
            return

        # Copy more attributes
        req.logprob_start_len = len(req.origin_input_ids) - 1
        self._add_request_to_queue(req)

    def log_prefill_stats(
        self,
        adder: PrefillAdder,
        can_run_list: List[Req],
        running_bs: int,
    ):
        num_used = self.max_total_num_tokens - (
            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
        )
        self._largest_prefill_len = max(
            self._largest_prefill_len, adder.log_input_tokens
        )

        f = (
            f"Prefill batch. "
            f"#new-seq: {len(can_run_list)}, "
            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}, "
            f"#queue-req: {len(self.waiting_queue)}, "
        )
        logger.info(f)

        if self.enable_metrics:
            cache_hit_rate = adder.log_hit_tokens / (
                adder.log_input_tokens + adder.log_hit_tokens
            )
            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)
            self.stats.num_queue_reqs = len(self.waiting_queue)
            self.stats.cache_hit_rate = cache_hit_rate
            self.metrics_collector.log_stats(self.stats)

    def log_decode_stats(self):
        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
        num_running_reqs = len(self.running_batch.reqs) if self.running_batch else 0
        num_used = self.max_total_num_tokens - (
            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
        )

        if RECORD_STEP_TIME:
            self.step_time_dict[num_running_reqs].append(
                gap_latency / self.server_args.decode_log_interval
            )

        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}, "
                f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
                f"largest-len: {self._largest_prefill_decode_len}, "
                f"#queue-req: {len(self.waiting_queue)}, "
            )
            spec_accept_length = 0
        else:
            spec_accept_length = (
                self.spec_num_total_accepted_tokens / self.spec_num_total_forward_ct
            )
            self.cum_spec_accept_length += self.spec_num_total_accepted_tokens
            self.cum_spec_accept_count += self.spec_num_total_forward_ct
            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}, "
                f"accept len: {spec_accept_length:.2f}, "
                f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
                f"largest-len: {self._largest_prefill_decode_len}, "
                f"#queue-req: {len(self.waiting_queue)}, "
            )

        logger.info(msg)
        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
            self.stats.cache_hit_rate = 0.0
            self.stats.gen_throughput = self.last_gen_throughput
            self.stats.num_queue_reqs = len(self.waiting_queue)
            self.stats.spec_accept_length = spec_accept_length
            self.metrics_collector.log_stats(self.stats)

    def check_memory(self):
        available_size = (
            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
        )
        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:
            msg = (
                "KV cache pool leak detected!"
                f"{available_size=}, {protected_size=}, {self.max_total_num_tokens=}\n"
            )
            warnings.warn(msg)
            if crash_on_warnings():
                raise ValueError(msg)

        if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
            msg = (
                "Memory pool leak detected!"
                f"available_size={len(self.req_to_token_pool.free_slots)}, "
                f"total_size={self.req_to_token_pool.size}\n"
            )
            warnings.warn(msg)
            if crash_on_warnings():
                raise ValueError(msg)

        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 - (
                self.token_to_kv_pool.available_size()
                + self.tree_cache.evictable_size()
            )
            num_running_reqs = len(self.running_batch.reqs) if self.running_batch else 0
            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)

    def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
        # Merge the prefill batch into the running batch
        if self.last_batch and self.last_batch.forward_mode.is_extend():
            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)
                self.batch_is_full = False

            self.last_batch.filter_batch()
            if not self.last_batch.is_empty():
                if self.running_batch is None:
                    self.running_batch = self.last_batch
                else:
                    # merge running_batch with prefill batch
                    self.running_batch.merge_batch(self.last_batch)

        new_batch = self.get_new_batch_prefill()
        if new_batch is not None:
            # Run prefill first if possible
            ret = new_batch
        else:
            # Run decode
            if self.running_batch is None:
                ret = None
            else:
                self.running_batch = self.update_running_batch(self.running_batch)
                ret = self.running_batch

        # Handle DP attention
        if self.server_args.enable_dp_attention:
            ret = self.prepare_dp_attn_batch(ret)

        return ret

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

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

        running_bs = len(self.running_batch.reqs) if self.running_batch else 0
        if running_bs >= self.max_running_requests:
            self.batch_is_full = True
            return None

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

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

        is_chunked = self.chunked_req is not None
        if is_chunked:
            self.chunked_req.init_next_round_input()
            self.chunked_req = adder.add_chunked_req(self.chunked_req)

        if self.lora_paths:
            lora_set = (
                set([req.lora_path for req in self.running_batch.reqs])
                if self.running_batch is not None
                else set([])
            )
        # Get requests from the waiting queue to a new prefill batch
        for req in self.waiting_queue:
            if (
                self.lora_paths
                and len(
                    lora_set
                    | set([req.lora_path for req in adder.can_run_list])
                    | set([req.lora_path])
                )
                > self.max_loras_per_batch
            ):
                self.batch_is_full = True
                break

            if running_bs + len(adder.can_run_list) >= self.max_running_requests:
                self.batch_is_full = True
                break

            req.init_next_round_input(None if prefix_computed else self.tree_cache)

            if self.enable_hierarchical_cache and req.last_node is not None:
                if req.last_node.evicted:
                    # loading KV cache for the request
                    req.last_node, req.prefix_indices = self.tree_cache.init_load_back(
                        req.last_node,
                        req.prefix_indices,
                        adder.rem_total_tokens,
                    )
                    if req.last_node.loading:
                        # to prevent frequent cache invalidation
                        if req.rid in self.staging_reqs:
                            self.tree_cache.dec_lock_ref(self.staging_reqs[req.rid])
                        self.tree_cache.inc_lock_ref(req.last_node)
                        self.staging_reqs[req.rid] = req.last_node
                        continue
                elif req.last_node.loading:
                    if not self.tree_cache.loading_complete(req.last_node):
                        continue

                if req.rid in self.staging_reqs:
                    self.tree_cache.dec_lock_ref(self.staging_reqs[req.rid])
                    del self.staging_reqs[req.rid]

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

        # Update waiting queue
        can_run_list: List[Req] = adder.can_run_list
        if len(can_run_list) == 0:
            return None
        self.waiting_queue = [
            x for x in self.waiting_queue if x not in set(can_run_list)
        ]

        if adder.new_chunked_req is not None:
            assert self.chunked_req is None
            self.chunked_req = adder.new_chunked_req

        if self.chunked_req:
            self.chunked_req.is_chunked += 1

        # Print stats
        if self.attn_tp_rank == 0:
            self.log_prefill_stats(adder, can_run_list, running_bs)

        # Create a new batch
        new_batch = ScheduleBatch.init_new(
            can_run_list,
            self.req_to_token_pool,
            self.token_to_kv_pool_allocator,
            self.tree_cache,
            self.model_config,
            self.enable_overlap,
            self.spec_algorithm,
            self.server_args.enable_custom_logit_processor,
        )
        new_batch.prepare_for_extend()

        # Mixed-style chunked prefill
        if (
            self.is_mixed_chunk
            and self.running_batch is not None
            and not (new_batch.return_logprob or self.running_batch.return_logprob)
        ):
            # TODO (lianmin): support return_logprob + mixed chunked prefill
            self.running_batch.filter_batch()
            if not self.running_batch.is_empty():
                self.running_batch.prepare_for_decode()
                new_batch.mix_with_running(self.running_batch)
                new_batch.decoding_reqs = self.running_batch.reqs
            self.running_batch = None
        else:
            new_batch.decoding_reqs = None

        return new_batch

    def update_running_batch(self, batch: ScheduleBatch) -> Optional[ScheduleBatch]:
        """Update the current running decoding batch."""
        initial_bs = batch.batch_size()

        batch.filter_batch()
        if batch.is_empty():
            self.batch_is_full = False
            return None

        # Check if decode out of memory
        if not batch.check_decode_mem(self.decode_mem_cache_buf_multiplier) or (
            TEST_RETRACT and batch.batch_size() > 10
        ):
            old_ratio = self.new_token_ratio

            retracted_reqs, new_token_ratio = batch.retract_decode(self.server_args)
            self.new_token_ratio = new_token_ratio

            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}"
            )
            self._extend_requests_to_queue(retracted_reqs)
        else:
            self.new_token_ratio = max(
                self.new_token_ratio - self.new_token_ratio_decay,
                self.min_new_token_ratio,
            )

        if batch.batch_size() < initial_bs:
            self.batch_is_full = False

        # Update batch tensors
        batch.prepare_for_decode()
        return batch

    def run_batch(
        self, batch: ScheduleBatch
    ) -> Union[GenerationBatchResult, EmbeddingBatchResult]:
        """Run a batch."""
        self.forward_ct += 1

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

        if self.is_generation:
            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
                )
                bid = model_worker_batch.bid
            else:
                (
                    logits_output,
                    next_token_ids,
                    bid,
                    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
            batch.output_ids = next_token_ids
            # 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

            ret = GenerationBatchResult(
                logits_output=logits_output,
                next_token_ids=next_token_ids,
                extend_input_len_per_req=extend_input_len_per_req,
                extend_logprob_start_len_per_req=extend_logprob_start_len_per_req,
                bid=bid,
            )
        else:  # embedding or reward model
            model_worker_batch = batch.get_model_worker_batch()
            embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
            ret = EmbeddingBatchResult(
                embeddings=embeddings, bid=model_worker_batch.bid
            )
        return ret

    def process_batch_result(
        self,
        batch: ScheduleBatch,
        result: Union[GenerationBatchResult, EmbeddingBatchResult],
    ):
        if batch.forward_mode.is_decode():
            assert isinstance(result, GenerationBatchResult)
            self.process_batch_result_decode(batch, result)
            if batch.is_empty():
                self.running_batch = None
        elif batch.forward_mode.is_extend():
            self.process_batch_result_prefill(batch, result)
        elif batch.forward_mode.is_idle():
            if self.enable_overlap:
                self.tp_worker.resolve_batch_result(result.bid)
                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()
        elif batch.forward_mode.is_dummy_first():
            batch.next_batch_sampling_info.update_regex_vocab_mask()
            self.current_stream.synchronize()
            batch.next_batch_sampling_info.sampling_info_done.set()

        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())

    def process_batch_result_prefill(
        self,
        batch: ScheduleBatch,
        result: Union[GenerationBatchResult, EmbeddingBatchResult],
    ):
        skip_stream_req = None

        if self.is_generation:
            (
                logits_output,
                next_token_ids,
                extend_input_len_per_req,
                extend_logprob_start_len_per_req,
                bid,
            ) = (
                result.logits_output,
                result.next_token_ids,
                result.extend_input_len_per_req,
                result.extend_logprob_start_len_per_req,
                result.bid,
            )

            if self.enable_overlap:
                logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
            else:
                # Move next_token_ids and logprobs to cpu
                next_token_ids = next_token_ids.tolist()
                if batch.return_logprob:
                    if logits_output.next_token_logprobs is not None:
                        logits_output.next_token_logprobs = (
                            logits_output.next_token_logprobs.tolist()
                        )
                    if logits_output.input_token_logprobs is not None:
                        logits_output.input_token_logprobs = tuple(
                            logits_output.input_token_logprobs.tolist()
                        )

            hidden_state_offset = 0

            # Check finish conditions
            logprob_pt = 0
            for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
                if req.is_retracted:
                    continue

                if self.is_mixed_chunk and self.enable_overlap and req.finished():
                    # Free the one delayed token for the mixed decode batch
                    j = len(batch.out_cache_loc) - len(batch.reqs) + i
                    self.token_to_kv_pool_allocator.free(batch.out_cache_loc[j : j + 1])
                    continue

                if req.is_chunked <= 0:
                    # req output_ids are set here
                    req.output_ids.append(next_token_id)
                    req.check_finished()

                    if req.finished():
                        self.tree_cache.cache_finished_req(req)
                    elif not batch.decoding_reqs or req not in batch.decoding_reqs:
                        # This updates radix so others can match
                        self.tree_cache.cache_unfinished_req(req)

                    if req.return_logprob:
                        assert extend_logprob_start_len_per_req is not None
                        assert extend_input_len_per_req is not None
                        extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                        extend_input_len = extend_input_len_per_req[i]
                        num_input_logprobs = extend_input_len - extend_logprob_start_len
                        self.add_logprob_return_values(
                            i,
                            req,
                            logprob_pt,
                            next_token_ids,
                            num_input_logprobs,
                            logits_output,
                        )
                        logprob_pt += num_input_logprobs

                    if (
                        req.return_hidden_states
                        and logits_output.hidden_states is not None
                    ):
                        req.hidden_states.append(
                            logits_output.hidden_states[
                                hidden_state_offset : (
                                    hidden_state_offset := hidden_state_offset
                                    + len(req.origin_input_ids)
                                )
                            ]
                            .cpu()
                            .clone()
                        )

                    if req.grammar is not None:
                        req.grammar.accept_token(next_token_id)
                        req.grammar.finished = req.finished()
                else:
                    # being chunked reqs' prefill is not finished
                    req.is_chunked -= 1
                    # There is only at most one request being currently chunked.
                    # Because this request does not finish prefill,
                    # we don't want to stream the request currently being chunked.
                    skip_stream_req = req

                    # Incrementally update input logprobs.
                    if req.return_logprob:
                        extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                        extend_input_len = extend_input_len_per_req[i]
                        if extend_logprob_start_len < extend_input_len:
                            # Update input logprobs.
                            num_input_logprobs = (
                                extend_input_len - extend_logprob_start_len
                            )
                            self.add_input_logprob_return_values(
                                i,
                                req,
                                logits_output,
                                logprob_pt,
                                num_input_logprobs,
                                last_prefill_chunk=False,
                            )
                            logprob_pt += num_input_logprobs

            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()

        else:  # embedding or reward model
            embeddings, bid = result.embeddings, result.bid
            embeddings = embeddings.tolist()

            # Check finish conditions
            for i, req in enumerate(batch.reqs):
                if req.is_retracted:
                    continue

                req.embedding = embeddings[i]
                if req.is_chunked <= 0:
                    # Dummy output token for embedding models
                    req.output_ids.append(0)
                    req.check_finished()

                    if req.finished():
                        self.tree_cache.cache_finished_req(req)
                    else:
                        self.tree_cache.cache_unfinished_req(req)
                else:
                    # being chunked reqs' prefill is not finished
                    req.is_chunked -= 1

        self.stream_output(batch.reqs, batch.return_logprob, skip_stream_req)

    def process_batch_result_decode(
        self,
        batch: ScheduleBatch,
        result: GenerationBatchResult,
    ):
        logits_output, next_token_ids, bid = (
            result.logits_output,
            result.next_token_ids,
            result.bid,
        )
        self.num_generated_tokens += len(batch.reqs)

        if self.enable_overlap:
            assert batch.spec_algorithm.is_none()
            logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
            next_token_logprobs = logits_output.next_token_logprobs
        elif batch.spec_algorithm.is_none():
            # spec decoding handles output logprobs inside verify process.
            next_token_ids = next_token_ids.tolist()
            if batch.return_logprob:
                next_token_logprobs = logits_output.next_token_logprobs.tolist()

        self.token_to_kv_pool_allocator.free_group_begin()

        # Check finish condition
        # NOTE: the length of reqs and next_token_ids don't match if it is spec decoding.
        # We should ignore using next_token_ids for spec decoding cases.
        for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
            if req.is_retracted:
                continue

            if self.enable_overlap and req.finished():
                # Free the one delayed token
                self.token_to_kv_pool_allocator.free(batch.out_cache_loc[i : i + 1])
                continue

            if batch.spec_algorithm.is_none():
                # speculative worker will solve the output_ids in speculative decoding
                req.output_ids.append(next_token_id)

            req.check_finished()
            if req.finished():
                self.tree_cache.cache_finished_req(req)

            if req.return_logprob and batch.spec_algorithm.is_none():
                # speculative worker handles logprob in speculative decoding
                req.output_token_logprobs_val.append(next_token_logprobs[i])
                req.output_token_logprobs_idx.append(next_token_id)
                if req.top_logprobs_num > 0:
                    req.output_top_logprobs_val.append(
                        logits_output.next_token_top_logprobs_val[i]
                    )
                    req.output_top_logprobs_idx.append(
                        logits_output.next_token_top_logprobs_idx[i]
                    )
                if req.token_ids_logprob is not None:
                    req.output_token_ids_logprobs_val.append(
                        logits_output.next_token_token_ids_logprobs_val[i]
                    )
                    req.output_token_ids_logprobs_idx.append(
                        logits_output.next_token_token_ids_logprobs_idx[i]
                    )

            if req.return_hidden_states and logits_output.hidden_states is not None:
                req.hidden_states.append(logits_output.hidden_states[i].cpu().clone())

            if req.grammar is not None and batch.spec_algorithm.is_none():
                req.grammar.accept_token(next_token_id)
                req.grammar.finished = req.finished()

        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()
        self.stream_output(batch.reqs, batch.return_logprob)

        self.token_to_kv_pool_allocator.free_group_end()

        self.forward_ct_decode = (self.forward_ct_decode + 1) % (1 << 30)
        if (
            self.attn_tp_rank == 0
            and self.forward_ct_decode % self.server_args.decode_log_interval == 0
        ):
            self.log_decode_stats()

    def add_input_logprob_return_values(
        self,
        i: int,
        req: Req,
        output: LogitsProcessorOutput,
        logprob_pt: int,
        num_input_logprobs: int,
        last_prefill_chunk: bool,  # If True, it means prefill is finished.
    ):
        """Incrementally add input logprobs to `req`.

        Args:
            i: The request index in a batch.
            req: The request. Input logprobs inside req are modified as a
                consequence of the API
            fill_ids: The prefill ids processed.
            output: Logit processor output that's used to compute input logprobs
            last_prefill_chunk: True if it is the last prefill (when chunked).
                Some of input logprob operation should only happen at the last
                prefill (e.g., computing input token logprobs).
        """
        assert output.input_token_logprobs is not None
        if req.input_token_logprobs is None:
            req.input_token_logprobs = []
        if req.temp_input_top_logprobs_val is None:
            req.temp_input_top_logprobs_val = []
        if req.temp_input_top_logprobs_idx is None:
            req.temp_input_top_logprobs_idx = []
        if req.temp_input_token_ids_logprobs_val is None:
            req.temp_input_token_ids_logprobs_val = []
        if req.temp_input_token_ids_logprobs_idx is None:
            req.temp_input_token_ids_logprobs_idx = []

        if req.input_token_logprobs_val is not None:
            # The input logprob has been already computed. It only happens
            # upon retract.
            if req.top_logprobs_num > 0:
                assert req.input_token_logprobs_val is not None
            return

        # Important for the performance.
        assert isinstance(output.input_token_logprobs, tuple)
        input_token_logprobs: Tuple[int] = output.input_token_logprobs
        input_token_logprobs = input_token_logprobs[
            logprob_pt : logprob_pt + num_input_logprobs
        ]
        req.input_token_logprobs.extend(input_token_logprobs)

        if req.top_logprobs_num > 0:
            req.temp_input_top_logprobs_val.append(output.input_top_logprobs_val[i])
            req.temp_input_top_logprobs_idx.append(output.input_top_logprobs_idx[i])

        if req.token_ids_logprob is not None:
            req.temp_input_token_ids_logprobs_val.append(
                output.input_token_ids_logprobs_val[i]
            )
            req.temp_input_token_ids_logprobs_idx.append(
                output.input_token_ids_logprobs_idx[i]
            )

        if last_prefill_chunk:
            input_token_logprobs = req.input_token_logprobs
            req.input_token_logprobs = None
            assert req.input_token_logprobs_val is None
            assert req.input_token_logprobs_idx is None
            assert req.input_top_logprobs_val is None
            assert req.input_top_logprobs_idx is None

            # Compute input_token_logprobs_val
            # Always pad the first one with None.
            req.input_token_logprobs_val = [None]
            req.input_token_logprobs_val.extend(input_token_logprobs)
            # The last input logprob is for sampling, so just pop it out.
            req.input_token_logprobs_val.pop()

            # Compute input_token_logprobs_idx
            input_token_logprobs_idx = req.origin_input_ids[req.logprob_start_len :]
            # Clip the padded hash values from image tokens.
            # Otherwise, it will lead to detokenization errors.
            input_token_logprobs_idx = [
                x if x < self.model_config.vocab_size - 1 else 0
                for x in input_token_logprobs_idx
            ]
            req.input_token_logprobs_idx = input_token_logprobs_idx

            if req.top_logprobs_num > 0:
                req.input_top_logprobs_val = [None]
                req.input_top_logprobs_idx = [None]
                assert len(req.temp_input_token_ids_logprobs_val) == len(
                    req.temp_input_token_ids_logprobs_idx
                )
                for val, idx in zip(
                    req.temp_input_top_logprobs_val, req.temp_input_top_logprobs_idx
                ):
                    req.input_top_logprobs_val.extend(val)
                    req.input_top_logprobs_idx.extend(idx)

                # Last token is a sample token.
                req.input_top_logprobs_val.pop()
                req.input_top_logprobs_idx.pop()
                req.temp_input_top_logprobs_idx = None
                req.temp_input_top_logprobs_val = None

            if req.token_ids_logprob is not None:
                req.input_token_ids_logprobs_val = [None]
                req.input_token_ids_logprobs_idx = [None]

                for val, idx in zip(
                    req.temp_input_token_ids_logprobs_val,
                    req.temp_input_token_ids_logprobs_idx,
                    strict=True,
                ):
                    req.input_token_ids_logprobs_val.extend(val)
                    req.input_token_ids_logprobs_idx.extend(idx)

                # Last token is a sample token.
                req.input_token_ids_logprobs_val.pop()
                req.input_token_ids_logprobs_idx.pop()
                req.temp_input_token_ids_logprobs_idx = None
                req.temp_input_token_ids_logprobs_val = None

            if req.return_logprob:
                relevant_tokens_len = len(req.origin_input_ids) - req.logprob_start_len
                assert len(req.input_token_logprobs_val) == relevant_tokens_len
                assert len(req.input_token_logprobs_idx) == relevant_tokens_len
                if req.top_logprobs_num > 0:
                    assert len(req.input_top_logprobs_val) == relevant_tokens_len
                    assert len(req.input_top_logprobs_idx) == relevant_tokens_len
                if req.token_ids_logprob is not None:
                    assert len(req.input_token_ids_logprobs_val) == relevant_tokens_len
                    assert len(req.input_token_ids_logprobs_idx) == relevant_tokens_len

    def add_logprob_return_values(
        self,
        i: int,
        req: Req,
        pt: int,
        next_token_ids: List[int],
        num_input_logprobs: int,
        output: LogitsProcessorOutput,
    ):
        """Attach logprobs to the return values."""
        req.output_token_logprobs_val.append(output.next_token_logprobs[i])
        req.output_token_logprobs_idx.append(next_token_ids[i])

        self.add_input_logprob_return_values(
            i, req, output, pt, num_input_logprobs, last_prefill_chunk=True
        )

        if req.top_logprobs_num > 0:
            req.output_top_logprobs_val.append(output.next_token_top_logprobs_val[i])
            req.output_top_logprobs_idx.append(output.next_token_top_logprobs_idx[i])

        if req.token_ids_logprob is not None:
            req.output_token_ids_logprobs_val.append(
                output.next_token_token_ids_logprobs_val[i]
            )
            req.output_token_ids_logprobs_idx.append(
                output.next_token_token_ids_logprobs_idx[i]
            )

        return num_input_logprobs

    def stream_output(
        self, reqs: List[Req], return_logprob: bool, skip_req: Optional[Req] = None
    ):
        """Stream the output to detokenizer."""
        rids = []
        finished_reasons: List[BaseFinishReason] = []

        if self.is_generation:
            decoded_texts = []
            decode_ids_list = []
            read_offsets = []
            output_ids = []

            skip_special_tokens = []
            spaces_between_special_tokens = []
            no_stop_trim = []
            prompt_tokens = []
            completion_tokens = []
            cached_tokens = []
            spec_verify_ct = []
            output_hidden_states = None

            if return_logprob:
                input_token_logprobs_val = []
                input_token_logprobs_idx = []
                output_token_logprobs_val = []
                output_token_logprobs_idx = []
                input_top_logprobs_val = []
                input_top_logprobs_idx = []
                output_top_logprobs_val = []
                output_top_logprobs_idx = []
                input_token_ids_logprobs_val = []
                input_token_ids_logprobs_idx = []
                output_token_ids_logprobs_val = []
                output_token_ids_logprobs_idx = []
            else:
                input_token_logprobs_val = input_token_logprobs_idx = (
                    output_token_logprobs_val
                ) = output_token_logprobs_idx = input_top_logprobs_val = (
                    input_top_logprobs_idx
                ) = output_top_logprobs_val = output_top_logprobs_idx = (
                    input_token_ids_logprobs_val
                ) = input_token_ids_logprobs_idx = output_token_ids_logprobs_val = (
                    output_token_ids_logprobs_idx
                ) = None

            for req in reqs:
                if req is skip_req:
                    continue

                # Multimodal partial stream chunks break the detokenizer, so drop aborted requests here.
                if self.model_config.is_multimodal_gen and req.to_abort:
                    continue

                if (
                    req.finished()
                    # If stream, follow the given stream_interval
                    or (req.stream and len(req.output_ids) % self.stream_interval == 0)
                    # If not stream, we still want to output some tokens to get the benefit of incremental decoding.
                    # TODO(lianmin): this is wrong for speculative decoding because len(req.output_ids) does not
                    # always increase one-by-one.
                    or (
                        not req.stream
                        and len(req.output_ids) % 50 == 0
                        and not self.model_config.is_multimodal_gen
                    )
                ):
                    rids.append(req.rid)
                    finished_reasons.append(
                        req.finished_reason.to_json() if req.finished_reason else None
                    )
                    decoded_texts.append(req.decoded_text)
                    decode_ids, read_offset = req.init_incremental_detokenize()
                    decode_ids_list.append(decode_ids)
                    read_offsets.append(read_offset)
                    if self.skip_tokenizer_init:
                        output_ids.append(req.output_ids)
                    skip_special_tokens.append(req.sampling_params.skip_special_tokens)
                    spaces_between_special_tokens.append(
                        req.sampling_params.spaces_between_special_tokens
                    )
                    no_stop_trim.append(req.sampling_params.no_stop_trim)

                    prompt_tokens.append(len(req.origin_input_ids))
                    completion_tokens.append(len(req.output_ids))
                    cached_tokens.append(req.cached_tokens)

                    if not self.spec_algorithm.is_none():
                        spec_verify_ct.append(req.spec_verify_ct)

                    if return_logprob:
                        input_token_logprobs_val.append(req.input_token_logprobs_val)
                        input_token_logprobs_idx.append(req.input_token_logprobs_idx)
                        output_token_logprobs_val.append(req.output_token_logprobs_val)
                        output_token_logprobs_idx.append(req.output_token_logprobs_idx)
                        input_top_logprobs_val.append(req.input_top_logprobs_val)
                        input_top_logprobs_idx.append(req.input_top_logprobs_idx)
                        output_top_logprobs_val.append(req.output_top_logprobs_val)
                        output_top_logprobs_idx.append(req.output_top_logprobs_idx)
                        input_token_ids_logprobs_val.append(
                            req.input_token_ids_logprobs_val
                        )
                        input_token_ids_logprobs_idx.append(
                            req.input_token_ids_logprobs_idx
                        )
                        output_token_ids_logprobs_val.append(
                            req.output_token_ids_logprobs_val
                        )
                        output_token_ids_logprobs_idx.append(
                            req.output_token_ids_logprobs_idx
                        )

                    if req.return_hidden_states:
                        if output_hidden_states is None:
                            output_hidden_states = []
                        output_hidden_states.append(req.hidden_states)

            # Send to detokenizer
            if rids:
                if self.model_config.is_multimodal_gen:
                    raise NotImplementedError()
                self.send_to_detokenizer.send_pyobj(
                    BatchTokenIDOut(
                        rids,
                        finished_reasons,
                        decoded_texts,
                        decode_ids_list,
                        read_offsets,
                        output_ids,
                        skip_special_tokens,
                        spaces_between_special_tokens,
                        no_stop_trim,
                        prompt_tokens,
                        completion_tokens,
                        cached_tokens,
                        spec_verify_ct,
                        input_token_logprobs_val,
                        input_token_logprobs_idx,
                        output_token_logprobs_val,
                        output_token_logprobs_idx,
                        input_top_logprobs_val,
                        input_top_logprobs_idx,
                        output_top_logprobs_val,
                        output_top_logprobs_idx,
                        input_token_ids_logprobs_val,
                        input_token_ids_logprobs_idx,
                        output_token_ids_logprobs_val,
                        output_token_ids_logprobs_idx,
                        output_hidden_states,
                    )
                )
        else:  # embedding or reward model
            embeddings = []
            prompt_tokens = []
            for req in reqs:
                if req.finished():
                    rids.append(req.rid)
                    finished_reasons.append(req.finished_reason.to_json())
                    embeddings.append(req.embedding)
                    prompt_tokens.append(len(req.origin_input_ids))
            self.send_to_detokenizer.send_pyobj(
                BatchEmbeddingOut(rids, finished_reasons, embeddings, prompt_tokens)
            )

    def prepare_dp_attn_batch(self, local_batch: ScheduleBatch):
        # Check if other DP workers have running batches
        if local_batch is None:
            num_tokens = 0
        elif local_batch.forward_mode.is_decode():
            num_tokens = local_batch.batch_size()
        else:
            num_tokens = local_batch.extend_num_tokens

        local_num_tokens = torch.tensor([num_tokens], dtype=torch.int64)
        global_num_tokens = torch.empty(self.tp_size, dtype=torch.int64)
        torch.distributed.all_gather_into_tensor(
            global_num_tokens,
            local_num_tokens,
            group=self.tp_cpu_group,
        )

        if local_batch is None and global_num_tokens.max().item() > 0:
            local_batch = self.get_idle_batch()

        if local_batch is not None:
            local_batch.global_num_tokens = global_num_tokens.tolist()

            # Check forward mode for cuda graph
            if not self.server_args.disable_cuda_graph:
                forward_mode_state = torch.tensor(
                    (1 if local_batch.forward_mode.is_decode_or_idle() else 0),
                    dtype=torch.int32,
                )
                torch.distributed.all_reduce(
                    forward_mode_state,
                    op=torch.distributed.ReduceOp.MIN,
                    group=self.tp_cpu_group,
                )
                local_batch.can_run_dp_cuda_graph = forward_mode_state.item() == 1

        return local_batch

    def get_idle_batch(self):
        idle_batch = ScheduleBatch.init_new(
            [],
            self.req_to_token_pool,
            self.token_to_kv_pool_allocator,
            self.tree_cache,
            self.model_config,
            self.enable_overlap,
            self.spec_algorithm,
            self.server_args.enable_custom_logit_processor,
        )
        idle_batch.prepare_for_idle()
        return idle_batch

    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

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

        self._extend_requests_to_queue(self.grammar_queue[:num_ready_reqs])
        self.grammar_queue = self.grammar_queue[num_ready_reqs:]

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

    def flush_cache(self):
        """Flush the memory pool and cache."""
        if len(self.waiting_queue) == 0 and (
            self.running_batch is None or len(self.running_batch.reqs) == 0
        ):
            self.cur_batch = None
            self.last_batch = None
            self.tree_cache.reset()
            self.tree_cache_metrics = {"total": 0, "hit": 0}
            if self.grammar_backend:
                self.grammar_backend.reset()
            self.req_to_token_pool.clear()
            self.token_to_kv_pool_allocator.clear()

            if not self.spec_algorithm.is_none():
                self.draft_worker.model_runner.req_to_token_pool.clear()
                self.draft_worker.model_runner.token_to_kv_pool_allocator.clear()

            self.num_generated_tokens = 0
            self.forward_ct_decode = 0
            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
            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)}, "
                f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
            )
            if_success = False
        return if_success

    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,
        )

    def abort_request(self, recv_req: AbortReq):
        # Delete requests in the waiting queue
        to_del = None
        for i, req in enumerate(self.waiting_queue):
            if req.rid == recv_req.rid:
                to_del = i
                break

        if to_del is not None:
            del self.waiting_queue[to_del]
            logger.debug(f"Abort queued request. {req.rid=}")
            return

        # Delete requests in the running batch
        if self.running_batch:
            for req in self.running_batch.reqs:
                if req.rid == recv_req.rid and not req.finished():
                    logger.debug(f"Abort running request. {req.rid=}")
                    req.to_abort = True
                    break

    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)
        if success:
            flash_cache_success = self.flush_cache()
            assert flash_cache_success, "Cache flush failed after updating weights"
        else:
            logger.error(message)
        return UpdateWeightFromDiskReqOutput(success, message, 0)

    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)
        return InitWeightsUpdateGroupReqOutput(success, message)

    def update_weights_from_distributed(
        self,
        recv_req: UpdateWeightsFromDistributedReqInput,
    ) -> Tuple[bool, str]:
        """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)
        return UpdateWeightsFromDistributedReqOutput(success, message)

    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:
            if recv_req.flush_cache:
                flash_cache_success = self.flush_cache()
                assert flash_cache_success, "Cache flush failed after updating weights"
        else:
            logger.error(message)
        return UpdateWeightsFromTensorReqOutput(success, message)

    def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
        parameter = self.tp_worker.get_weights_by_name(recv_req)
        return GetWeightsByNameReqOutput(parameter)

    def release_memory_occupation(self, recv_req: ReleaseMemoryOccupationReqInput):
        self.stashed_model_static_state = _export_static_state(
            self.tp_worker.worker.model_runner.model
        )
        self.memory_saver_adapter.pause()
        self.flush_cache()
        return ReleaseMemoryOccupationReqOutput()

    def resume_memory_occupation(self, recv_req: ResumeMemoryOccupationReqInput):
        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
        return ResumeMemoryOccupationReqOutput()

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

    def start_profile(
        self,
        output_dir: Optional[str],
        num_steps: Optional[int],
        activities: Optional[List[str]],
    ) -> None:
        if self.torch_profiler_activities:
            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
        self.torch_profiler_activities = activities
        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,
                with_stack=True,
            )
            self.torch_profiler.start()

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

        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")

    def stop_profile(self) -> None:
        if self.torch_profiler_activities is None:
            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",
                )
            )

        if "MEM" in self.torch_profiler_activities:
            memory_profile_path = os.path.join(
                self.torch_profiler_trace_dir,
                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)

        logger.info(
            "Profiling done. Traces are saved to: %s",
            self.torch_profiler_output_dir,
        )
        self.torch_profiler = None
        self.torch_profiler_output_dir = None
        self.torch_profiler_activities = None

        if self.profiler_target_forward_ct:
            self.send_to_tokenizer.send_pyobj(
                ProfileReqOutput(success=True, message="Succeeded.")
            )

    def open_session(self, recv_req: OpenSessionReqInput):
        # 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.")
            return OpenSessionReqOutput(session_id, False)
        elif session_id is None:
            logger.warning("session id is None, cannot open.")
            return OpenSessionReqOutput(session_id, False)
        else:
            self.sessions[session_id] = Session(
                recv_req.capacity_of_str_len, session_id
            )
            return OpenSessionReqOutput(session_id, True)

    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]


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


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


def run_scheduler_process(
    server_args: ServerArgs,
    port_args: PortArgs,
    gpu_id: int,
    tp_rank: int,
    dp_rank: Optional[int],
    pipe_writer,
):
    # Config the process
    # kill_itself_when_parent_died()  # This is disabled because it does not work for `--dp 2`
    setproctitle.setproctitle(f"sglang::scheduler_{dp_rank}")
    faulthandler.enable()
    parent_process = psutil.Process().parent()

    # [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"])

    # Configure the logger
    if dp_rank is None:
        prefix = f" TP{tp_rank}"
    else:
        prefix = f" DP{dp_rank} TP{tp_rank}"
    configure_logger(server_args, prefix=prefix)
    suppress_other_loggers()

    # Set cpu affinity to this gpu process
    if get_bool_env_var("SGLANG_SET_CPU_AFFINITY"):
        set_gpu_proc_affinity(server_args.tp_size, server_args.nnodes, gpu_id)

    # Create a scheduler and run the event loop
    try:
        scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank)
        pipe_writer.send(
            {
                "status": "ready",
                "max_total_num_tokens": scheduler.max_total_num_tokens,
                "max_req_input_len": scheduler.max_req_input_len,
            }
        )
        if scheduler.enable_overlap:
            scheduler.event_loop_overlap()
        else:
            scheduler.event_loop_normal()
    except Exception:
        traceback = get_exception_traceback()
        logger.error(f"Scheduler hit an exception: {traceback}")
        parent_process.send_signal(signal.SIGQUIT)