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sglang/python/sglang/srt/managers/scheduler.py
Tianyu Guo fa2966983a Support PP for zmq_to_scheduler (#15312)
2025-12-23 17:07:55 +08:00

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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.
# ==============================================================================
"""A scheduler that manages a tensor parallel GPU worker."""
import faulthandler
import logging
import os
import signal
import sys
import time
from collections import deque
from concurrent import futures
from dataclasses import dataclass
from http import HTTPStatus
from typing import Any, Deque, Dict, List, Optional, Tuple, Union
import psutil
import setproctitle
import torch
import torch.distributed
import zmq
from torch.cuda import Stream as CudaStream
from torch.cuda import StreamContext as CudaStreamContext
from torch.distributed import barrier
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.constrained.base_grammar_backend import (
INVALID_GRAMMAR_OBJ,
create_grammar_backend,
)
from sglang.srt.disaggregation.decode import (
DecodePreallocQueue,
DecodeTransferQueue,
SchedulerDisaggregationDecodeMixin,
)
from sglang.srt.disaggregation.decode_kvcache_offload_manager import (
DecodeKVCacheOffloadManager,
)
from sglang.srt.disaggregation.encode_receiver import MMReceiver
from sglang.srt.disaggregation.prefill import (
PrefillBootstrapQueue,
SchedulerDisaggregationPrefillMixin,
)
from sglang.srt.disaggregation.utils import (
DisaggregationMode,
MetadataBuffers,
ReqToMetadataIdxAllocator,
TransferBackend,
prepare_abort,
)
from sglang.srt.distributed import get_pp_group, get_world_group
from sglang.srt.dllm.config import DllmConfig
from sglang.srt.environ import envs
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
from sglang.srt.layers.moe import initialize_moe_config
from sglang.srt.layers.quantization.fp8_utils import initialize_fp8_gemm_config
from sglang.srt.managers.io_struct import (
AbortReq,
BaseBatchReq,
BaseReq,
BatchTokenizedEmbeddingReqInput,
BatchTokenizedGenerateReqInput,
CheckWeightsReqInput,
ClearHiCacheReqInput,
ClearHiCacheReqOutput,
CloseSessionReqInput,
ContinueGenerationReqInput,
DestroyWeightsUpdateGroupReqInput,
ExpertDistributionReq,
ExpertDistributionReqOutput,
ExpertDistributionReqType,
FlushCacheReqInput,
FlushCacheReqOutput,
FreezeGCReq,
GetInternalStateReq,
GetInternalStateReqOutput,
GetLoadReqInput,
GetWeightsByNameReqInput,
HealthCheckOutput,
InitWeightsSendGroupForRemoteInstanceReqInput,
InitWeightsSendGroupForRemoteInstanceReqOutput,
InitWeightsUpdateGroupReqInput,
LoadLoRAAdapterReqInput,
LoadLoRAAdapterReqOutput,
OpenSessionReqInput,
OpenSessionReqOutput,
PauseGenerationReqInput,
ProfileReq,
ReleaseMemoryOccupationReqInput,
ResumeMemoryOccupationReqInput,
RpcReqInput,
RpcReqOutput,
SendWeightsToRemoteInstanceReqInput,
SendWeightsToRemoteInstanceReqOutput,
SetInternalStateReq,
SetInternalStateReqOutput,
SlowDownReqInput,
SlowDownReqOutput,
TokenizedEmbeddingReqInput,
TokenizedGenerateReqInput,
UnloadLoRAAdapterReqInput,
UnloadLoRAAdapterReqOutput,
UpdateWeightFromDiskReqInput,
UpdateWeightsFromDistributedReqInput,
UpdateWeightsFromIPCReqInput,
UpdateWeightsFromTensorReqInput,
)
from sglang.srt.managers.mm_utils import init_mm_embedding_cache
from sglang.srt.managers.overlap_utils import FutureMap
from sglang.srt.managers.schedule_batch import (
FINISH_ABORT,
ModelWorkerBatch,
MultimodalInputs,
Req,
RequestStage,
ScheduleBatch,
)
from sglang.srt.managers.schedule_policy import (
AddReqResult,
PrefillAdder,
SchedulePolicy,
)
from sglang.srt.managers.scheduler_dp_attn_mixin import SchedulerDPAttnMixin
from sglang.srt.managers.scheduler_enhancer import SchedulerEnhancer
from sglang.srt.managers.scheduler_input_blocker import SchedulerInputBlocker
from sglang.srt.managers.scheduler_metrics_mixin import (
RECORD_STEP_TIME,
SchedulerMetricsMixin,
)
from sglang.srt.managers.scheduler_output_processor_mixin import (
SchedulerOutputProcessorMixin,
)
from sglang.srt.managers.scheduler_pp_mixin import SchedulerPPMixin
from sglang.srt.managers.scheduler_profiler_mixin import SchedulerProfilerMixin
from sglang.srt.managers.scheduler_recv_skipper import SchedulerRecvSkipper
from sglang.srt.managers.scheduler_runtime_checker_mixin import (
SchedulerRuntimeCheckerMixin,
create_scheduler_watchdog,
)
from sglang.srt.managers.scheduler_update_weights_mixin import (
SchedulerUpdateWeightsMixin,
)
from sglang.srt.managers.session_controller import Session
from sglang.srt.managers.utils import GenerationBatchResult, validate_input_length
from sglang.srt.mem_cache.cache_init_params import CacheInitParams
from sglang.srt.mem_cache.common import release_kv_cache
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.model_executor.forward_batch_info import ForwardMode, PPProxyTensors
from sglang.srt.multiplex.multiplexing_mixin import SchedulerMultiplexMixin
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.srt.server_args import PortArgs, ServerArgs, get_global_server_args
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.tracing.trace import (
process_tracing_init,
trace_event_batch,
trace_set_proc_propagate_context,
trace_set_thread_info,
trace_slice_batch,
trace_slice_end,
trace_slice_start,
)
from sglang.srt.utils import (
DynamicGradMode,
broadcast_pyobj,
configure_gc_logger,
configure_logger,
freeze_gc,
get_available_gpu_memory,
get_bool_env_var,
get_int_env_var,
get_zmq_socket,
kill_itself_when_parent_died,
numa_bind_to_node,
point_to_point_pyobj,
require_mlp_sync,
set_gpu_proc_affinity,
set_random_seed,
suppress_other_loggers,
)
from sglang.srt.utils.hf_transformers_utils import (
get_processor,
get_tokenizer,
get_tokenizer_from_processor,
)
from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
from sglang.utils import TypeBasedDispatcher, get_exception_traceback
logger = logging.getLogger(__name__)
# Test retract decode for debugging purposes
TEST_RETRACT = envs.SGLANG_TEST_RETRACT.get()
TEST_RETRACT_INTERVAL = envs.SGLANG_TEST_RETRACT_INTERVAL.get()
TEST_RETRACT_NO_PREFILL_BS = envs.SGLANG_TEST_RETRACT_NO_PREFILL_BS.get()
SCHEDULER_DECREASE_PREFILL_IDLE = envs.SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE.get()
GRAMMAR_TIMEOUT = float(os.environ.get("SGLANG_GRAMMAR_TIMEOUT", 300))
@dataclass
class EmbeddingBatchResult:
embeddings: torch.Tensor
class Scheduler(
SchedulerOutputProcessorMixin,
SchedulerUpdateWeightsMixin,
SchedulerProfilerMixin,
SchedulerMetricsMixin,
SchedulerDisaggregationDecodeMixin,
SchedulerDisaggregationPrefillMixin,
SchedulerMultiplexMixin,
SchedulerRuntimeCheckerMixin,
SchedulerPPMixin,
SchedulerDPAttnMixin,
):
"""A scheduler that manages a tensor parallel GPU worker."""
def __init__(
self,
server_args: ServerArgs,
port_args: PortArgs,
gpu_id: int,
tp_rank: int,
moe_ep_rank: int,
pp_rank: int,
dp_rank: Optional[int],
):
# Parse args
self.server_args = server_args
self.tp_rank = tp_rank
self.moe_ep_rank = moe_ep_rank
self.pp_rank = pp_rank
self.dp_rank = dp_rank
self.tp_size = server_args.tp_size
self.moe_ep_size = server_args.ep_size
self.pp_size = server_args.pp_size
self.dp_size = server_args.dp_size
self.nccl_port = port_args.nccl_port
self.schedule_policy = server_args.schedule_policy
self.enable_priority_scheduling = server_args.enable_priority_scheduling
self.abort_on_priority_when_disabled = (
server_args.abort_on_priority_when_disabled
)
self.schedule_low_priority_values_first = (
server_args.schedule_low_priority_values_first
)
self.priority_scheduling_preemption_threshold = (
server_args.priority_scheduling_preemption_threshold
)
self.enable_lora = server_args.enable_lora
self.max_loras_per_batch = server_args.max_loras_per_batch
self.enable_overlap = not server_args.disable_overlap_schedule
self.enable_pdmux = server_args.enable_pdmux
self.skip_tokenizer_init = server_args.skip_tokenizer_init
self.enable_metrics = server_args.enable_metrics
self.enable_metrics_for_all_schedulers = (
server_args.enable_metrics_for_all_schedulers
)
self.enable_kv_cache_events = bool(
server_args.kv_events_config and tp_rank == 0
)
self.enable_trace = server_args.enable_trace
self.stream_interval = server_args.stream_interval
self.spec_algorithm = SpeculativeAlgorithm.from_string(
server_args.speculative_algorithm
)
self.gpu_id = gpu_id
self.page_size = server_args.page_size
self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
self.enable_hicache_storage = server_args.hicache_storage_backend is not None
self.max_recv_per_poll = envs.SGLANG_SCHEDULER_MAX_RECV_PER_POLL.get()
# Distributed rank info
self.attn_tp_rank, self.attn_tp_size, self.attn_dp_rank = (
compute_dp_attention_world_info(
server_args.enable_dp_attention,
self.tp_rank,
self.tp_size,
self.dp_size,
)
)
# Init model configs
self.init_model_config()
# Init metrics stats
self.init_metrics(tp_rank, pp_rank, dp_rank)
# Init inter-process communication
self.init_sockets(server_args, port_args)
# Init PD-multiplexing context
if self.enable_pdmux:
self.init_pdmux()
# Init tokenizer
self.init_tokenizer()
# Init moe config and GEMM config (FP8 GEMM, etc.)
self.init_moe_gemm_config()
# Launch a model worker and draft model worker if using speculative decoding
self.init_model_worker()
# Init cache and memory pool
self.init_cache_with_memory_pool()
# Init running status
self.init_running_status()
# Init chunked prefill
self.init_chunked_prefill()
# Init the grammar backend for constrained generation
self.init_grammar_backend()
# Init schedule policy and new token estimation
self.init_schedule_policy()
# Init watchdog, memory saver, input blocker and recv skipper
self.init_watch_dog_memory_saver_input_blocker()
# Init profiler
self.init_profiler()
# Init prefill-decodedisaggregation
self.init_disaggregation()
# Init overlap schedule
self.init_overlap()
# Init prefill kv split size when deterministic inference is enabled with various attention backends
self.init_deterministic_inference_config()
# Init request dispatcher
self.init_request_dispatcher()
def init_model_config(self):
self.model_config = ModelConfig.from_server_args(self.server_args)
self.dllm_config = ( # For diffusion LLM
DllmConfig.from_server_args(self.server_args)
if self.server_args.dllm_algorithm is not None
else None
)
def init_sockets(self, server_args: ServerArgs, port_args: PortArgs):
context = zmq.Context(2)
self.idle_sleeper = None
if self.pp_rank == 0 and self.attn_tp_rank == 0:
self.recv_from_tokenizer = get_zmq_socket(
context, zmq.PULL, port_args.scheduler_input_ipc_name, False
)
self.recv_from_rpc = get_zmq_socket(
context, zmq.DEALER, port_args.rpc_ipc_name, False
)
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
send_to_detokenizer = get_zmq_socket(
context, zmq.PUSH, port_args.tokenizer_ipc_name, False
)
else:
# Send to the DetokenizerManager
send_to_detokenizer = get_zmq_socket(
context, zmq.PUSH, port_args.detokenizer_ipc_name, False
)
self.send_to_tokenizer = SenderWrapper(send_to_tokenizer)
self.send_to_detokenizer = SenderWrapper(send_to_detokenizer)
if self.server_args.sleep_on_idle:
self.idle_sleeper = IdleSleeper(
[
self.recv_from_tokenizer,
self.recv_from_rpc,
]
)
else:
self.recv_from_tokenizer = None
self.recv_from_rpc = None
self.send_to_tokenizer = SenderWrapper(None)
self.send_to_detokenizer = SenderWrapper(None)
if self.current_scheduler_metrics_enabled:
self.send_metrics_from_scheduler = get_zmq_socket(
context, zmq.PUSH, port_args.metrics_ipc_name, False
)
def init_tokenizer(self):
server_args = self.server_args
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,
use_fast=not server_args.disable_fast_image_processor,
)
self.tokenizer = get_tokenizer_from_processor(self.processor)
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,
)
# 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]
def init_moe_gemm_config(self):
if hasattr(self.model_config.hf_config, "num_experts_per_tok"):
initialize_moe_config(self.server_args)
# Initialize GEMM-related configuration (currently FP8 Blockwise GEMM backend).
# Other GEMM backends (e.g. FP4, BF16, etc.) can be added here in the future.
# This is needed for FP8 quantization.
initialize_fp8_gemm_config(self.server_args)
# This must be called after initialize_moe_config
self.require_mlp_sync = require_mlp_sync(self.server_args)
def init_model_worker(self):
from sglang.srt.managers.tp_worker import TpModelWorker
self.tp_worker = TpModelWorker(
server_args=self.server_args,
gpu_id=self.gpu_id,
tp_rank=self.tp_rank,
moe_ep_rank=self.moe_ep_rank,
pp_rank=self.pp_rank,
dp_rank=self.dp_rank,
nccl_port=self.nccl_port,
)
# Launch a draft worker for speculative decoding
draft_worker_kwargs = dict(
server_args=self.server_args,
gpu_id=self.gpu_id,
tp_rank=self.tp_rank,
moe_ep_rank=self.moe_ep_rank,
nccl_port=self.nccl_port,
target_worker=self.tp_worker,
dp_rank=self.dp_rank,
)
if self.server_args.speculative_draft_load_format is not None:
self.server_args.load_format = (
self.server_args.speculative_draft_load_format
)
logger.info(
f"Using draft model load_format: '{self.server_args.speculative_draft_load_format}'"
)
# Draft workers are looked up via `SpeculativeAlgorithm` registry; new
# algorithms should register their factory instead of patching this code.
if self.spec_algorithm.is_eagle():
draft_worker_kwargs["enable_overlap"] = self.enable_overlap
# FIXME: refactor the draft worker registration logic
if self.server_args.enable_multi_layer_eagle:
if self.enable_overlap:
from sglang.srt.speculative.multi_layer_eagle_worker_v2 import (
MultiLayerEagleWorkerV2,
)
self.draft_worker = MultiLayerEagleWorkerV2(
gpu_id=self.gpu_id,
tp_rank=self.tp_rank,
moe_ep_rank=self.moe_ep_rank,
server_args=self.server_args,
nccl_port=self.nccl_port,
target_worker=self.tp_worker,
dp_rank=self.dp_rank,
)
else:
from sglang.srt.speculative.multi_layer_eagle_worker import (
MultiLayerEagleWorker,
)
self.draft_worker = MultiLayerEagleWorker(
gpu_id=self.gpu_id,
tp_rank=self.tp_rank,
moe_ep_rank=self.moe_ep_rank,
server_args=self.server_args,
nccl_port=self.nccl_port,
target_worker=self.tp_worker,
dp_rank=self.dp_rank,
)
else:
self.draft_worker = self.spec_algorithm.create_draft_worker(
**draft_worker_kwargs
)
# Dispatch the model worker
if self.spec_algorithm.is_none():
self.model_worker = self.tp_worker
else:
self.model_worker = self.draft_worker
# Get token and memory info from the model worker
(
self.max_total_num_tokens,
self.max_prefill_tokens,
self.max_running_requests,
self.max_queued_requests,
self.max_req_len,
self.max_req_input_len,
self.random_seed,
self.device,
_,
_,
_,
) = self.tp_worker.get_worker_info()
if get_global_server_args().pp_max_micro_batch_size is None:
get_global_server_args().pp_max_micro_batch_size = max(
self.max_running_requests // self.pp_size, 1
)
self.tp_group = self.tp_worker.get_tp_group()
self.tp_cpu_group = self.tp_group.cpu_group
self.attn_tp_group = self.tp_worker.get_attention_tp_group()
self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group()
self.pp_group = get_pp_group()
self.world_group = get_world_group()
# With DP attention enabled, the entry rank is attn_tp_rank==0;
# otherwise the entry rank is TP group local rank 0.
# For #11910, use the CPU communication group to broadcast VLM Python objects,
# avoiding any coupling with CUDA streams/devices.
if self.server_args.enable_dp_attention:
self.cpu_group = self.attn_tp_cpu_group
self.entry_rank = self.attn_tp_group.first_rank
self.is_entry_rank = self.attn_tp_rank == 0
else:
self.cpu_group = self.tp_cpu_group
self.entry_rank = self.tp_group.first_rank
self.is_entry_rank = self.tp_group.rank_in_group == 0
self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
set_random_seed(self.random_seed)
# Print debug info
if self.tp_rank == 0:
avail_mem = get_available_gpu_memory(
self.device, self.gpu_id, empty_cache=False
)
logger.info(
f"max_total_num_tokens={self.max_total_num_tokens}, "
f"chunked_prefill_size={self.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}, "
f"{'available_cpu_mem' if self.device == 'cpu' else 'available_gpu_mem'}={avail_mem:.2f} GB"
)
def init_cache_with_memory_pool(self):
server_args = self.server_args
# Hybrid memory pool
self.is_hybrid_swa = self.tp_worker.is_hybrid_swa
self.is_hybrid_ssm = (
self.tp_worker.model_runner.hybrid_gdn_config is not None
or self.tp_worker.model_runner.mamba2_config is not None
)
if self.is_hybrid_swa:
self.sliding_window_size = self.tp_worker.sliding_window_size
self.full_tokens_per_layer, self.swa_tokens_per_layer = (
self.tp_worker.get_tokens_per_layer_info()
)
self.req_to_token_pool, self.token_to_kv_pool_allocator = (
self.tp_worker.get_memory_pool()
)
# Create cache
params = CacheInitParams(
disable=server_args.disable_radix_cache,
req_to_token_pool=self.req_to_token_pool,
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
page_size=self.page_size,
is_eagle=self.spec_algorithm.is_eagle(),
tp_cache_group=(
self.attn_tp_cpu_group
if self.server_args.enable_dp_attention
else self.tp_cpu_group
),
eviction_policy=server_args.radix_eviction_policy,
enable_metrics=self.enable_metrics,
enable_kv_cache_events=self.enable_kv_cache_events,
enable_mamba_extra_buffer=server_args.enable_mamba_extra_buffer(),
)
if (
server_args.chunked_prefill_size is not None
and server_args.disable_radix_cache
):
if not self.is_hybrid_swa:
from sglang.srt.mem_cache.chunk_cache import ChunkCache
self.tree_cache = ChunkCache(params)
else:
from sglang.srt.mem_cache.chunk_cache import SWAChunkCache
params.is_local_attention = (
"Llama4ForConditionalGeneration"
in self.model_config.hf_config.architectures
)
self.tree_cache = SWAChunkCache(params)
else:
if envs.SGLANG_EXPERIMENTAL_CPP_RADIX_TREE.get():
# lazy import to avoid JIT overhead
from sglang.srt.mem_cache.radix_cache_cpp import RadixCacheCpp
logger.info("Using experimental C++ radix tree implementation.")
self.tree_cache = RadixCacheCpp(params=params, server_args=server_args)
elif self.enable_hierarchical_cache:
from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
self.tree_cache = HiRadixCache(params=params, server_args=server_args)
self.tp_worker.register_hicache_layer_transfer_counter(
self.tree_cache.cache_controller.layer_done_counter
)
elif self.is_hybrid_swa:
from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
self.tree_cache = SWARadixCache(
params=params, sliding_window_size=self.sliding_window_size
)
elif self.is_hybrid_ssm:
from sglang.srt.mem_cache.mamba_radix_cache import MambaRadixCache
self.tree_cache = MambaRadixCache(params)
elif server_args.enable_lmcache:
from sglang.srt.mem_cache.storage.lmcache.lmc_radix_cache import (
LMCRadixCache,
)
self.tree_cache = LMCRadixCache(
params=params,
model_config=self.model_config,
tp_size=self.tp_size,
rank=self.tp_rank,
tp_group=self.tp_group,
)
else:
self.tree_cache = RadixCache(params)
if (
server_args.disaggregation_mode == "decode"
and server_args.disaggregation_decode_enable_offload_kvcache
):
self.decode_offload_manager = DecodeKVCacheOffloadManager(
req_to_token_pool=self.req_to_token_pool,
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
tp_group=params.tp_cache_group,
tree_cache=self.tree_cache,
server_args=self.server_args,
)
else:
self.decode_offload_manager = None
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 or 1)
* (server_args.speculative_num_steps or 1)
)
)
)
embedding_cache_size = envs.SGLANG_VLM_CACHE_SIZE_MB.get()
init_mm_embedding_cache(embedding_cache_size * 1024 * 1024)
def init_running_status(self):
self.waiting_queue: List[Req] = []
# The running decoding batch for continuous batching
self.running_batch: ScheduleBatch = ScheduleBatch(reqs=[], batch_is_full=False)
# The current forward batch
self.cur_batch: Optional[ScheduleBatch] = None
# The current split prefill batch
self.split_prefill_batch: Optional[ScheduleBatch] = None
# The last forward batch
self.last_batch: Optional[ScheduleBatch] = None
self.forward_ct = 0
self.last_prefill_tokens = 0
self.return_health_check_ct = 0
self.num_retracted_reqs: int = 0
self.num_paused_reqs: int = 0
self.sessions: Dict[str, Session] = {}
self.forward_sleep_time = None
self._engine_paused = False
def init_chunked_prefill(self):
# Init chunked prefill
self.chunked_prefill_size = self.server_args.chunked_prefill_size
if self.dllm_config is not None:
# We currently leverage chunked prefill to implement block diffusion
# for diffusion LLM.
self.chunked_prefill_size = self.dllm_config.block_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 self.server_args.enable_mixed_chunk
)
# Init the dynamic chunking predictor for PP
self.enable_dynamic_chunking = (
self.server_args.enable_dynamic_chunking and self.pp_size > 1
)
if self.enable_dynamic_chunking:
try:
self.profile_and_init_predictor()
except Exception as e:
logger.warning(
f"[PP Dynamic Chunk] Failed to profile prefill latency: {e}. "
"Dynamic chunking will be disabled."
)
self.enable_dynamic_chunking = False
def init_grammar_backend(self):
self.grammar_queue: List[Req] = []
if not self.server_args.skip_tokenizer_init:
self.grammar_backend = create_grammar_backend(
self.server_args,
self.tokenizer,
self.model_config.vocab_size,
self.model_config.hf_eos_token_id,
)
else:
self.grammar_backend = None
def init_schedule_policy(self):
# Init schedule policy and new token estimation
self.policy = SchedulePolicy(
self.schedule_policy,
self.tree_cache,
self.enable_hierarchical_cache,
self.enable_priority_scheduling,
self.schedule_low_priority_values_first,
)
self.schedule_enhancer = None
if SCHEDULER_DECREASE_PREFILL_IDLE:
self.schedule_enhancer = SchedulerEnhancer(
self.dp_size,
self.attn_tp_size,
self.tp_worker,
self.max_running_requests,
self.server_args,
)
# Enable preemption for priority scheduling.
self.try_preemption = self.enable_priority_scheduling
self.init_new_token_ratio = min(
envs.SGLANG_INIT_NEW_TOKEN_RATIO.get()
* self.server_args.schedule_conservativeness,
1.0,
)
self.min_new_token_ratio = min(
self.init_new_token_ratio * envs.SGLANG_MIN_NEW_TOKEN_RATIO_FACTOR.get(),
1.0,
)
self.new_token_ratio_decay = (
self.init_new_token_ratio - self.min_new_token_ratio
) / envs.SGLANG_NEW_TOKEN_RATIO_DECAY_STEPS.get()
self.new_token_ratio = self.init_new_token_ratio
def init_watch_dog_memory_saver_input_blocker(self):
# Start watchdog thread
self.watchdog = create_scheduler_watchdog(
self, watchdog_timeout=self.server_args.watchdog_timeout
)
if (x := self.server_args.soft_watchdog_timeout) is not None:
self.soft_watchdog = create_scheduler_watchdog(
self, watchdog_timeout=x, soft=True
)
# Init memory saver, profiler and metric stats
self.memory_saver_adapter = TorchMemorySaverAdapter.create(
enable=self.server_args.enable_memory_saver
)
self.offload_tags = set()
# Init recv skipper and input blocker
self.recv_skipper = SchedulerRecvSkipper.maybe_create(self.server_args)
self.input_blocker = (
SchedulerInputBlocker(noop=self.attn_tp_rank != 0)
if get_bool_env_var("SGLANG_ENABLE_COLOCATED_BATCH_GEN")
else None
)
# Configure GC logger
if envs.SGLANG_LOG_GC.get():
configure_gc_logger()
def init_disaggregation(self):
self.disaggregation_mode = DisaggregationMode(
self.server_args.disaggregation_mode
)
self.transfer_backend = TransferBackend(
self.server_args.disaggregation_transfer_backend
)
if self.draft_worker is None or self.spec_algorithm.is_ngram():
draft_token_to_kv_pool = None
elif self.spec_algorithm.is_eagle() and self.enable_overlap:
if self.server_args.enable_multi_layer_eagle:
draft_runner = self.draft_worker.draft_worker.draft_runner_list[0]
else:
draft_runner = self.draft_worker.draft_worker.draft_runner
draft_token_to_kv_pool = draft_runner.token_to_kv_pool
model_config = draft_runner.model_config
else:
# todo: should we fix this when enabling mtp or it doesn't matter since we only enable mtp in decode node thus we don't transfer draft kvs between P and D?
draft_token_to_kv_pool = self.draft_worker.model_runner.token_to_kv_pool
model_config = self.draft_worker.model_config
if (
self.disaggregation_mode == DisaggregationMode.DECODE
): # *2 for the headroom.
buffer_size = (self.req_to_token_pool.size) * 2
self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
buffer_size
)
self.disagg_metadata_buffers = MetadataBuffers(
buffer_size,
hidden_size=(
model_config.hidden_size
if self.spec_algorithm.is_eagle()
else 16 # minimal padding size for RDMA
),
hidden_states_dtype=(
model_config.dtype
if self.spec_algorithm.is_eagle()
else torch.float32
),
custom_mem_pool=self.token_to_kv_pool_allocator.get_kvcache().maybe_get_custom_mem_pool(),
)
# The decode requests polling kv cache
self.disagg_decode_transfer_queue = DecodeTransferQueue(
gloo_group=self.attn_tp_cpu_group,
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
tp_rank=self.tp_rank,
metadata_buffers=self.disagg_metadata_buffers,
scheduler=self,
tree_cache=self.tree_cache,
)
# 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,
draft_token_to_kv_pool=draft_token_to_kv_pool,
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
metadata_buffers=self.disagg_metadata_buffers,
scheduler=self,
transfer_queue=self.disagg_decode_transfer_queue,
tree_cache=self.tree_cache,
gloo_group=self.attn_tp_cpu_group,
tp_rank=self.tp_rank,
tp_size=self.tp_size,
dp_size=self.server_args.dp_size,
gpu_id=self.gpu_id,
bootstrap_port=self.server_args.disaggregation_bootstrap_port,
max_total_num_tokens=self.max_total_num_tokens,
prefill_pp_size=self.server_args.disaggregation_prefill_pp,
pp_rank=self.pp_rank,
num_reserved_decode_tokens=self.server_args.num_reserved_decode_tokens,
transfer_backend=self.transfer_backend,
)
elif self.disaggregation_mode == DisaggregationMode.PREFILL:
# *2 for the headroom.
buffer_size = self.max_running_requests * 2
self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
buffer_size
)
self.disagg_metadata_buffers = MetadataBuffers(
buffer_size,
hidden_size=(
model_config.hidden_size
if self.spec_algorithm.is_eagle()
else 16 # minimal padding size for RDMA
),
hidden_states_dtype=(
model_config.dtype
if self.spec_algorithm.is_eagle()
else torch.float32
),
custom_mem_pool=self.token_to_kv_pool_allocator.get_kvcache().maybe_get_custom_mem_pool(),
)
self.disagg_prefill_bootstrap_queue = PrefillBootstrapQueue(
token_to_kv_pool=self.token_to_kv_pool_allocator.get_kvcache(),
draft_token_to_kv_pool=draft_token_to_kv_pool,
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
metadata_buffers=self.disagg_metadata_buffers,
tp_rank=self.tp_rank,
tp_size=self.tp_size,
gpu_id=self.gpu_id,
bootstrap_port=self.server_args.disaggregation_bootstrap_port,
gloo_group=self.attn_tp_cpu_group,
max_total_num_tokens=self.max_total_num_tokens,
decode_tp_size=self.server_args.disaggregation_decode_tp,
decode_dp_size=self.server_args.disaggregation_decode_dp,
scheduler=self,
pp_rank=self.pp_rank,
pp_size=self.pp_size,
transfer_backend=self.transfer_backend,
)
# The prefill requests that are in the middle of kv sending
self.disagg_prefill_inflight_queue: List[Req] = []
# Init mm receiver for EPD disaggregation mode
if (
self.server_args.language_only
and self.server_args.encoder_transfer_backend == "zmq_to_scheduler"
):
self.mm_receiver = MMReceiver(
self.server_args,
hf_config=self.model_config.hf_config,
tp_rank=self.tp_rank,
pp_rank=self.pp_rank,
tp_group=self.tp_group,
)
def init_overlap(self):
self.device_module = torch.get_device_module(self.device)
self.default_stream: CudaStream = self.device_module.current_stream()
if self.device == "cpu":
self.default_stream.synchronize = lambda: None # No-op for CPU
self.forward_stream: CudaStream = self.device_module.Stream()
self.forward_stream_ctx: CudaStreamContext = self.device_module.stream(
self.forward_stream
)
self.copy_stream: CudaStream = self.device_module.Stream()
self.copy_stream_ctx: CudaStreamContext = self.device_module.stream(
self.copy_stream
)
if not self.enable_overlap:
self.future_map = None
return
self.future_map = FutureMap(
self.max_running_requests,
self.chunked_prefill_size,
self.model_config.context_len,
self.device,
self.spec_algorithm,
)
self.batch_record_buf = [None] * 2
self.batch_record_ct = 0
def init_deterministic_inference_config(self):
"""Initialize deterministic inference configuration for different attention backends."""
if not self.server_args.enable_deterministic_inference:
self.truncation_align_size = None
return
backend_sizes = {
"flashinfer": ("SGLANG_FLASHINFER_PREFILL_SPLIT_TILE_SIZE", 4096),
"triton": ("SGLANG_TRITON_PREFILL_TRUNCATION_ALIGN_SIZE", 4096),
}
env_var, default_size = backend_sizes.get(
self.server_args.attention_backend, (None, None)
)
self.truncation_align_size = (
get_int_env_var(env_var, default_size) if env_var else None
)
def init_request_dispatcher(self):
self._request_dispatcher = TypeBasedDispatcher(
[
(TokenizedGenerateReqInput, self.handle_generate_request),
(TokenizedEmbeddingReqInput, self.handle_embedding_request),
(BatchTokenizedGenerateReqInput, self.handle_batch_generate_request),
(BatchTokenizedEmbeddingReqInput, self.handle_batch_embedding_request),
(FlushCacheReqInput, self.flush_cache_wrapped),
(ClearHiCacheReqInput, self.clear_hicache_storage_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),
(DestroyWeightsUpdateGroupReqInput, self.destroy_weights_update_group),
(
InitWeightsSendGroupForRemoteInstanceReqInput,
self.init_weights_send_group_for_remote_instance,
),
(
SendWeightsToRemoteInstanceReqInput,
self.send_weights_to_remote_instance,
),
(
UpdateWeightsFromDistributedReqInput,
self.update_weights_from_distributed,
),
(UpdateWeightsFromTensorReqInput, self.update_weights_from_tensor),
(UpdateWeightsFromIPCReqInput, self.update_weights_from_ipc),
(GetWeightsByNameReqInput, self.get_weights_by_name),
(ReleaseMemoryOccupationReqInput, self.release_memory_occupation),
(ResumeMemoryOccupationReqInput, self.resume_memory_occupation),
(CheckWeightsReqInput, self.check_weights),
(SlowDownReqInput, self.slow_down),
(ProfileReq, self.profile),
(FreezeGCReq, self.handle_freeze_gc),
(GetInternalStateReq, self.get_internal_state),
(SetInternalStateReq, self.set_internal_state),
(RpcReqInput, self.handle_rpc_request),
(ExpertDistributionReq, self.expert_distribution_handle),
(LoadLoRAAdapterReqInput, self.load_lora_adapter),
(UnloadLoRAAdapterReqInput, self.unload_lora_adapter),
(GetLoadReqInput, self.get_load),
(PauseGenerationReqInput, self.pause_generation),
(ContinueGenerationReqInput, self.continue_generation),
]
)
@DynamicGradMode()
def event_loop_normal(self):
"""A normal scheduler loop."""
while True:
# Receive requests
recv_reqs = self.recv_requests()
self.process_input_requests(recv_reqs)
if self._engine_paused:
continue
# Get the next batch to run
batch = self.get_next_batch_to_run()
self.cur_batch = batch
# Launch the current batch
if batch:
result = self.run_batch(batch)
self.process_batch_result(batch, result)
else:
# When the server is idle, do self-check and re-init some states
self.self_check_during_idle()
# Update last_batch
self.last_batch = batch
if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
self.self_check_during_busy()
@DynamicGradMode()
def event_loop_overlap(self):
"""A scheduler loop that overlaps the CPU processing and GPU computation."""
self.result_queue: Deque[Tuple[ScheduleBatch, GenerationBatchResult]] = deque()
def pop_and_process():
# Process the results of the last batch
tmp_batch, tmp_result = self.result_queue.popleft()
self.process_batch_result(tmp_batch, tmp_result)
while True:
# Receive requests
recv_reqs = self.recv_requests()
self.process_input_requests(recv_reqs)
if self._engine_paused:
continue
# Get the next batch to run
batch = self.get_next_batch_to_run()
self.cur_batch = batch
disable_overlap_for_batch = self.is_disable_overlap_for_batch(batch)
# If we do not need to overlap the current batch with the last batch,
# we can process the last batch immediately.
if disable_overlap_for_batch:
pop_and_process()
# Launch the current batch
if batch:
batch_result = self.run_batch(batch)
self.result_queue.append((batch.copy(), batch_result))
else:
batch_result = None
# Process the last batch
if self.last_batch:
if not disable_overlap_for_batch:
pop_and_process()
elif batch is None:
# When the server is idle, do self-check and re-init some states
self.self_check_during_idle()
# Run sample of the current batch
# It depends on the result of the last batch (e.g., grammar), so we run it after the last batch is processed.
self.launch_batch_sample_if_needed(batch_result)
# Update last_batch
self.last_batch = batch
if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
self.self_check_during_busy()
def is_disable_overlap_for_batch(self, batch: ScheduleBatch) -> bool:
# For two consecutive prefill batches, we disable overlap to improve the TTFT of the first batch.
# This might slightly hurt the throughput, so we use an environment variable to control it.
disable_overlap_for_batch = (
envs.SGLANG_DISABLE_CONSECUTIVE_PREFILL_OVERLAP.get()
and batch
and batch.forward_mode.is_extend()
and self.last_batch
and self.last_batch.forward_mode.is_extend()
)
# We do not support overlap + spec + grammar yet,
# so we need to turn off overlap for this batch.
# TODO(lsyin): support overlap + spec + grammar
need_grammar_sync = (
batch
and batch.is_eagle_v2
and batch.has_grammar
and batch.forward_mode.is_decode()
and len(self.result_queue) > 0
)
return disable_overlap_for_batch or need_grammar_sync
def recv_limit_reached(self, num_recv_reqs: int) -> bool:
if self.max_recv_per_poll < 0:
return False
return num_recv_reqs >= self.max_recv_per_poll
def recv_requests(
self,
) -> List[Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput, Any]]:
"""Receive results at tp_rank = 0 and broadcast it to all other TP ranks."""
if self.recv_skipper is not None:
last_forward_mode = (
self.last_batch.forward_mode if self.last_batch is not None else None
)
if not self.recv_skipper.handle(last_forward_mode):
return []
if self.pp_rank == 0:
if self.attn_tp_rank == 0:
recv_reqs = []
while True:
try:
if self.recv_limit_reached(len(recv_reqs)):
break
recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
except zmq.ZMQError:
break
recv_reqs.append(recv_req)
while True:
try:
if self.recv_limit_reached(len(recv_reqs)):
break
recv_rpc = self.recv_from_rpc.recv_pyobj(zmq.NOBLOCK)
except zmq.ZMQError:
break
recv_reqs.append(recv_rpc)
else:
recv_reqs = None
else:
if self.attn_tp_rank == 0:
dp_offset = self.attn_dp_rank * self.attn_tp_size
recv_reqs = point_to_point_pyobj(
[],
self.pp_rank * self.tp_size + dp_offset,
self.world_group.cpu_group,
(self.pp_rank - 1) * self.tp_size + dp_offset,
self.pp_rank * self.tp_size + dp_offset,
)
else:
recv_reqs = None
if self.input_blocker is not None:
recv_reqs = self.input_blocker.handle(recv_reqs)
if self.server_args.enable_dp_attention:
if self.attn_tp_rank == 0:
work_reqs, control_reqs = self._split_work_and_control_reqs(recv_reqs)
else:
work_reqs = None
control_reqs = None
if self.attn_tp_size != 1:
work_reqs = broadcast_pyobj(
work_reqs,
self.attn_tp_group.rank,
self.attn_tp_cpu_group,
src=self.attn_tp_group.ranks[0],
)
if self.tp_size != 1:
control_reqs = broadcast_pyobj(
control_reqs,
self.tp_group.rank,
self.tp_cpu_group,
src=self.tp_group.ranks[0],
)
recv_reqs = work_reqs + control_reqs
elif self.tp_size != 1:
recv_reqs = broadcast_pyobj(
recv_reqs,
self.tp_group.rank,
self.tp_cpu_group,
src=self.tp_group.ranks[0],
)
# Process MM requests under EPD-disaggregation mode
if (
self.pp_rank == 0
and self.server_args.language_only
and self.server_args.encoder_transfer_backend == "zmq_to_scheduler"
):
recv_reqs = self.mm_receiver.process_waiting_requests(recv_reqs)
if self.enable_trace:
for req in recv_reqs:
if isinstance(
req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
):
trace_set_proc_propagate_context(req.rid, req.trace_context)
trace_slice_start("", req.rid, anonymous=True)
return recv_reqs
def _split_work_and_control_reqs(self, recv_reqs: List):
work_reqs = [
req
for req in recv_reqs
if isinstance(
req,
(
TokenizedGenerateReqInput,
TokenizedEmbeddingReqInput,
BatchTokenizedGenerateReqInput,
BatchTokenizedEmbeddingReqInput,
),
)
]
control_reqs = [
req
for req in recv_reqs
if not isinstance(
req,
(
TokenizedGenerateReqInput,
TokenizedEmbeddingReqInput,
BatchTokenizedGenerateReqInput,
BatchTokenizedEmbeddingReqInput,
),
)
]
return work_reqs, control_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 not self.running_batch.is_empty()
or len(self.offload_tags) > 0
):
self.return_health_check_ct += 1
continue
output = self._request_dispatcher(recv_req)
if output is not None:
if not isinstance(output, RpcReqOutput):
self.send_to_tokenizer.send_output(output, recv_req)
else:
if self.recv_from_rpc is not None:
self.recv_from_rpc.send_pyobj(output)
def init_req_max_new_tokens(self, req):
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,
)
def _process_and_broadcast_mm_inputs(
self,
raw_mm_inputs: Optional[dict],
):
"""Materialize MultimodalInputs once on the entry rank and broadcast to others.
Entry rank:
- constructs MultimodalInputs.from_dict(raw_mm_inputs) once
- broadcasts to other ranks in self.cpu_group (if world_size > 1)
Non-entry ranks:
- receive the object via broadcast (if world_size > 1)
- otherwise (single-rank / no group) fall back to local from_dict
Returns:
MultimodalInputs | None
"""
if raw_mm_inputs is None:
return None
group_world_size = 1
try:
if (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and self.cpu_group is not None
):
group_world_size = torch.distributed.get_world_size(
group=self.cpu_group
)
except Exception as e:
logger.warning(
f"Failed to get world size in mm_inputs handling with {e}, fallback to 1."
)
# In case tp size > 1, all the Scheduler TP ranks runs the duplicated computing
# process in CPU which occupies the main thread CPU cycle. This computing logic
# merely needs to be run on TP0 and be broadcast to other TP ranks.
# Since the Scheduler is single-threaded, any large CPU cost will impact
# handling of other messages. For example, CPU hits 99.9% can significantly
# increase the CUDA kernel launch time.
if self.is_entry_rank:
# Only the entry rank materializes once from dict.
image_inputs = MultimodalInputs.from_dict(raw_mm_inputs)
# Broadcast to other TP ranks (use src=0 within the group).
if group_world_size > 1:
obj_list = [image_inputs]
torch.distributed.broadcast_object_list(
obj_list, src=self.entry_rank, group=self.cpu_group
)
image_inputs = obj_list[0]
else:
# Non-entry ranks: receive if group size > 1; otherwise materialize locally.
if group_world_size > 1:
obj_list = [None]
torch.distributed.broadcast_object_list(
obj_list, src=self.entry_rank, group=self.cpu_group
)
image_inputs = obj_list[0]
else:
image_inputs = MultimodalInputs.from_dict(raw_mm_inputs)
return image_inputs
def _get_multimodal_inputs(self, mm_inputs_dict: dict):
if self.server_args.enable_broadcast_mm_inputs_process:
return self._process_and_broadcast_mm_inputs(mm_inputs_dict)
else:
return MultimodalInputs.from_dict(mm_inputs_dict)
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
if recv_req.bootstrap_port is None:
# Use default bootstrap port
recv_req.bootstrap_port = self.server_args.disaggregation_bootstrap_port
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_id=recv_req.lora_id,
input_embeds=recv_req.input_embeds,
custom_logit_processor=recv_req.custom_logit_processor,
require_reasoning=recv_req.require_reasoning,
return_hidden_states=recv_req.return_hidden_states,
return_routed_experts=recv_req.return_routed_experts,
eos_token_ids=self.model_config.hf_eos_token_id,
bootstrap_host=recv_req.bootstrap_host,
bootstrap_port=recv_req.bootstrap_port,
bootstrap_room=recv_req.bootstrap_room,
disagg_mode=self.disaggregation_mode,
data_parallel_rank=recv_req.data_parallel_rank,
vocab_size=self.model_config.vocab_size,
priority=recv_req.priority,
metrics_collector=(
self.metrics_collector if self.enable_metrics else None
),
http_worker_ipc=recv_req.http_worker_ipc,
dllm_config=self.dllm_config,
)
req.tokenizer = self.tokenizer
if self.disaggregation_mode != DisaggregationMode.NULL:
# Invalid request for disaggregated mode
if recv_req.bootstrap_room is None:
error_msg = (
f"Invalid request: Disaggregated request received without "
f"boostrap room id. {req.rid=}"
)
logger.error(error_msg)
prepare_abort(req, error_msg, status_code=HTTPStatus.BAD_REQUEST)
self.stream_output([req], req.return_logprob)
return
if (
recv_req.session_params is not None
and recv_req.session_params.id is not None
):
req.set_finish_with_abort(
f"Invalid request: session id {recv_req.session_params.id} does not exist"
)
self.init_req_max_new_tokens(req)
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.init_req_max_new_tokens(req)
self._add_request_to_queue(req)
return
# Handle multimodal inputs
if recv_req.mm_inputs is not None:
image_inputs = self._get_multimodal_inputs(recv_req.mm_inputs)
# The following steps are already fast, execute locally on each rank.
# 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:
req.set_finish_with_abort(
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}."
)
)
self.init_req_max_new_tokens(req)
self._add_request_to_queue(req)
return
# initialize before returning
self.init_req_max_new_tokens(req)
# Validate prompt length
error_msg = validate_input_length(
req,
self.max_req_input_len,
self.server_args.allow_auto_truncate,
)
if error_msg:
req.set_finish_with_abort(error_msg)
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
# For prefill-only requests with logprob_start_len == -1, set logprob_start_len beyond input sequence
# to skip input logprob computation entirely
if req.is_prefill_only:
req.logprob_start_len = len(req.origin_input_ids)
else:
# TODO: For text generation, evaluate setting logprob_start_len to len(req.origin_input_ids) as well
req.logprob_start_len = len(req.origin_input_ids) - 1
else:
req.logprob_start_len = recv_req.logprob_start_len
if not req.is_prefill_only and req.logprob_start_len >= len(
req.origin_input_ids
):
error_msg = f"{req.logprob_start_len=} is higher than the number of input tokens {len(req.origin_input_ids)=}. Please use a smaller logprob_start_len."
req.logprob_start_len = len(req.origin_input_ids) - 1
req.set_finish_with_abort(error_msg)
self._add_request_to_queue(req)
return
# 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
):
if self.grammar_backend is None:
error_msg = "Grammar-based generation (json_schema, regex, ebnf, structural_tag) is not supported when the server is launched with --grammar-backend none"
req.set_finish_with_abort(error_msg)
else:
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)
value, cache_hit = self.grammar_backend.get_cached_or_future_value(
key, req.require_reasoning
)
req.grammar = value
if not cache_hit:
req.grammar_key = key
add_to_grammar_queue = True
else:
if value is INVALID_GRAMMAR_OBJ: # We hit a cached invalid grammar.
error_msg = f"Invalid grammar request with cache hit: {key=}"
req.set_finish_with_abort(error_msg)
if add_to_grammar_queue:
self.grammar_queue.append(req)
else:
self._add_request_to_queue(req)
def handle_batch_generate_request(
self,
recv_req: BatchTokenizedGenerateReqInput,
):
"""Handle optimized batch generate request."""
logger.debug(f"Processing batch generate request with {len(recv_req)} requests")
# Process each request in the batch
for tokenized_req in recv_req:
self.handle_generate_request(tokenized_req)
def _prefetch_kvcache(self, req: Req):
if self.enable_hicache_storage:
req.init_next_round_input(self.tree_cache)
if req.last_node.backuped:
# only to initiate the prefetch if the last node is backuped
# otherwise, the allocated GPU memory must be locked for integrity
last_hash = req.last_host_node.get_last_hash_value()
matched_len = len(req.prefix_indices) + req.host_hit_length
new_input_tokens = req.fill_ids[matched_len:]
prefix_keys = (
req.last_node.get_prefix_hash_values(req.last_node.parent)
if self.tree_cache.hicache_storage_pass_prefix_keys
else None
)
self.tree_cache.prefetch_from_storage(
req.rid,
req.last_host_node,
new_input_tokens,
last_hash,
prefix_keys,
)
def _add_request_to_queue(self, req: Req, is_retracted: bool = False):
if self.disaggregation_mode == DisaggregationMode.NULL:
if not self._set_or_validate_priority(req):
return
if self._abort_on_queued_limit(req):
return
self._prefetch_kvcache(req)
self.waiting_queue.append(req)
req.time_stats.wait_queue_entry_time = time.perf_counter()
trace_slice_end(RequestStage.REQUEST_PROCESS, req.rid, auto_next_anon=True)
elif self.disaggregation_mode == DisaggregationMode.PREFILL:
self._prefetch_kvcache(req)
self.disagg_prefill_bootstrap_queue.add(
req, self.model_config.num_key_value_heads
)
req.time_stats.prefill_bootstrap_queue_entry_time = time.perf_counter()
elif self.disaggregation_mode == DisaggregationMode.DECODE:
self.disagg_decode_prealloc_queue.add(req, is_retracted=is_retracted)
if not is_retracted:
req.time_stats.decode_prealloc_queue_entry_time = time.perf_counter()
else:
raise ValueError(f"Invalid {self.disaggregation_mode=}")
def _set_or_validate_priority(self, req: Req) -> bool:
"""Set the default priority value, or abort the request based on the priority scheduling mode."""
if self.enable_priority_scheduling and req.priority is None:
if self.schedule_low_priority_values_first:
req.priority = sys.maxsize
else:
req.priority = -sys.maxsize - 1
elif (
not self.enable_priority_scheduling
and req.priority is not None
and self.abort_on_priority_when_disabled
):
abort_req = AbortReq(
finished_reason={
"type": "abort",
"status_code": HTTPStatus.SERVICE_UNAVAILABLE,
"message": "Using priority is disabled for this server. Please send a new request without a priority.",
},
rid=req.rid,
)
self.send_to_tokenizer.send_output(abort_req, req)
return False
return True
def _abort_on_queued_limit(self, recv_req: Req) -> bool:
"""Abort an incoming or existing request if the waiting queue is full. Returns True if the incoming request is aborted."""
if (
self.max_queued_requests is None
or len(self.waiting_queue) + 1 <= self.max_queued_requests
):
return False
# Reject the incoming request by default.
req_to_abort = recv_req
message = "The request queue is full."
if self.enable_priority_scheduling:
# With priority scheduling, consider aboritng an existing request based on the priority.
# direction = 1 => smaller number = higher priority; -1 => larger number = higher priority.
# max(...) + (direction * priority, queue_time_start) picks the least-preferred request.
# Tie: later queue_time_start (newer) is evicted first. Preempt only if strictly better.
direction = 1 if self.schedule_low_priority_values_first else -1
key_fn = lambda item: (
direction * item[1].priority,
item[1].time_stats.wait_queue_entry_time,
)
idx, candidate_req = max(enumerate(self.waiting_queue), key=key_fn)
abort_existing_req = (
direction * recv_req.priority < direction * candidate_req.priority
)
if abort_existing_req:
self.waiting_queue.pop(idx)
req_to_abort = candidate_req
message = "The request is aborted by a higher priority request."
self.send_to_tokenizer.send_output(
AbortReq(
finished_reason={
"type": "abort",
"status_code": HTTPStatus.SERVICE_UNAVAILABLE,
"message": message,
},
rid=req_to_abort.rid,
),
req_to_abort,
)
return req_to_abort.rid == recv_req.rid
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,
token_type_ids=recv_req.token_type_ids,
priority=recv_req.priority,
dimensions=recv_req.dimensions,
http_worker_ipc=recv_req.http_worker_ipc,
)
req.tokenizer = self.tokenizer
# Handle multimodal inputs
if recv_req.image_inputs is not None:
image_inputs = self._get_multimodal_inputs(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:
req.set_finish_with_abort(
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}."
)
)
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:
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 handle_batch_embedding_request(
self,
recv_req: BatchTokenizedEmbeddingReqInput,
):
"""Handle optimized batch embedding request."""
logger.debug(
f"Processing batch embedding request with {len(recv_req)} requests"
)
# Process each request in the batch
for tokenized_req in recv_req:
self.handle_embedding_request(tokenized_req)
def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
if self.dllm_config is not None:
if self.chunked_req is not None and self.chunked_req.finished():
self.chunked_req = None
# Merge the prefill batch into the running batch
chunked_req_to_exclude = set()
if self.chunked_req:
# Move the chunked request out of the batch so that we can merge
# only finished requests to running_batch.
chunked_req_to_exclude.add(self.chunked_req)
self.tree_cache.cache_unfinished_req(self.chunked_req, chunked=True)
# chunked request keeps its rid but will get a new req_pool_idx
if self.tp_worker.model_runner.mambaish_config is not None:
self.req_to_token_pool.free(
self.chunked_req.req_pool_idx, free_mamba_cache=False
)
else:
self.req_to_token_pool.free(self.chunked_req.req_pool_idx)
if self.last_batch and self.last_batch.forward_mode.is_extend():
if self.last_batch.chunked_req is not None:
# In the context pipeline parallelism, after the last chunk, the current microbatch still track outdated chunked_req.
# We need to discard it.
chunked_req_to_exclude.add(self.last_batch.chunked_req)
# Filter batch
last_bs = self.last_batch.batch_size()
self.last_batch.filter_batch(
chunked_req_to_exclude=list(chunked_req_to_exclude)
)
if self.last_batch.batch_size() < last_bs:
self.running_batch.batch_is_full = False
# Merge the new batch into the running batch.
# For prefill-only batch, we can avoid going through decoding step.
if not self.last_batch.is_empty() and not self.last_batch.is_prefill_only:
if self.running_batch.is_empty():
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()
need_mlp_sync = self.require_mlp_sync
if need_mlp_sync and not self.spec_algorithm.is_none():
# NOTE: This branch makes sure prefill and decode batches will not be mixed when spec and dp-attn is enabled.
# Before merging the new batch into running batch:
# 1. All new batches are none -> need_mlp_sync remains true (sync is needed for decode batch).
# 2. All new batches are some (prefill / idle) -> we do not need prepare mlp sync one more time.
new_batch = self.prepare_mlp_sync_batch(new_batch)
need_mlp_sync = new_batch is None
if new_batch is not None:
# Run prefill first if possible
ret = new_batch
else:
# Run decode
if not self.running_batch.is_empty():
self.running_batch = self.update_running_batch(self.running_batch)
ret = self.running_batch if not self.running_batch.is_empty() else None
else:
ret = None
# Handle DP attention
if need_mlp_sync:
ret = self.prepare_mlp_sync_batch(ret)
if ret:
trace_event_batch("schedule", ret.reqs)
return ret
def get_num_allocatable_reqs(self, running_bs):
res = get_global_server_args().pp_max_micro_batch_size - running_bs
if self.pp_size > 1:
res = min(res, self.req_to_token_pool.available_size())
return res
def get_new_batch_prefill(self) -> Optional[ScheduleBatch]:
if self.schedule_enhancer and not self.schedule_enhancer.get_schedule_decision(
self.running_batch
):
# Decrease prefill idle as much as possible during high dp load.
return None
# Check if the grammar is ready in the grammar queue
if self.grammar_queue:
self.move_ready_grammar_requests()
if self.try_preemption:
# Reset batch_is_full to try preemption with a prefill adder.
self.running_batch.batch_is_full = False
# Handle the cases where prefill is not allowed
if (
self.running_batch.batch_is_full or len(self.waiting_queue) == 0
) and self.chunked_req is None:
return None
running_bs = len(self.running_batch.reqs)
# Ignore the check if self.chunked_req is not None.
# In the non-PP case, when self.chunked_req is not None, num_allocatable_reqs should always be greater than 0,
# as the space for the chunked request has just been released.
# In PP case, a chunked req can start in one microbatch and end in another microbatch, so the max_running_requests per microbatch should not be strict.
# Instead, we should always allow chunked request to be added, otherwise, there will be a memory leak.
if (
self.get_num_allocatable_reqs(running_bs) <= 0
and not self.chunked_req
and not self.try_preemption
):
self.running_batch.batch_is_full = True
return None
if self.enable_hierarchical_cache:
self.tree_cache.check_hicache_events()
# Get priority queue
self.policy.calc_priority(self.waiting_queue)
if TEST_RETRACT and running_bs > TEST_RETRACT_NO_PREFILL_BS:
# If we are testing retraction and the running batch size exceeds
# TEST_RETRACT_NO_PREFILL_BS, we skip the prefill to keep the requests
# in the waiting queue.
return None
# Determine chunked_prefill_size for this batch
chunked_prefill_size = self.chunked_prefill_size
if self.chunked_req is not None and self.enable_dynamic_chunking:
history_len = len(self.chunked_req.prefix_indices)
dynamic_size = self.predict_next_chunk_size(history_len)
if dynamic_size is not None:
chunked_prefill_size = dynamic_size
# Prefill policy
adder = PrefillAdder(
self.page_size,
self.tree_cache,
self.token_to_kv_pool_allocator,
self.running_batch,
self.new_token_ratio,
self.max_prefill_tokens,
chunked_prefill_size,
running_bs if self.is_mixed_chunk else 0,
self.priority_scheduling_preemption_threshold,
prefill_max_requests=self.server_args.prefill_max_requests,
)
if self.chunked_req is not None:
self.chunked_req.init_next_round_input()
self.chunked_req = adder.add_chunked_req(self.chunked_req)
if self.enable_lora:
lora_set = set([req.lora_id for req in self.running_batch.reqs])
# Get requests from the waiting queue to a new prefill batch
for req in self.waiting_queue:
if self.enable_lora:
new_lora_set = (
lora_set
| set([req.lora_id for req in adder.can_run_list])
| set([req.lora_id])
)
if not self.tp_worker.can_run_lora_batch(new_lora_set):
# If this is a LoRA request that would exceed the LoRA slot limit,
# skip it and continue to try scheduling non-LoRA requests.
# Non-LoRA requests (lora_id=None) share a single reserved slot
# and should never cause this check to fail.
if req.lora_id is not None:
# Skip this LoRA request - it would trigger adapter eviction/loading
# which is slow. We'll try to schedule it in a future iteration.
continue
running_bs = len(self.running_batch.reqs)
if len(adder.can_run_list) >= self.get_num_allocatable_reqs(running_bs):
self.running_batch.batch_is_full = True
if self.disaggregation_mode == DisaggregationMode.PREFILL:
# In prefill mode, prealloc queue and transfer queue can also take memory,
# so we need to check if the available size for the actual available size.
if len(adder.can_run_list) >= self.req_to_token_pool.available_size():
self.running_batch.batch_is_full = True
if self.running_batch.batch_is_full:
if not self.try_preemption or not adder.preempt_to_schedule(
req, self.server_args
):
break
if self.enable_hicache_storage:
prefetch_done = self.tree_cache.check_prefetch_progress(req.rid)
if not prefetch_done:
# skip staging requests that are ongoing prefetch
continue
req.init_next_round_input(self.tree_cache)
res = adder.add_one_req(
req,
has_chunked_req=(self.chunked_req is not None),
truncation_align_size=self.truncation_align_size,
)
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.running_batch.batch_is_full = len(
adder.can_run_list
) > 0 or (not self.running_batch.is_empty())
else:
self.running_batch.batch_is_full = True
break
# Update waiting queue
can_run_list: List[Req] = adder.can_run_list
if len(can_run_list) == 0:
return None
if self.enable_metrics:
# only record queue time when enable_metrics is True to avoid overhead
for req in can_run_list:
req.add_latency(RequestStage.PREFILL_WAITING)
self.waiting_queue = [
x for x in self.waiting_queue if x not in set(can_run_list)
]
if adder.preempt_list:
for req in adder.preempt_list:
self._add_request_to_queue(req)
# Update chunked prefill
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.current_scheduler_metrics_enabled:
self.log_prefill_stats(adder, can_run_list, running_bs, 0)
# Record metrics
for req in can_run_list:
if req.time_stats.forward_entry_time == 0:
req.time_stats.forward_entry_time = time.perf_counter()
if self.enable_metrics:
self.metrics_collector.observe_queue_time(
req.time_stats.get_queueing_time(),
)
# 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,
chunked_req=self.chunked_req,
dllm_config=self.dllm_config,
)
if self.enable_hierarchical_cache:
# todo (zhiqiang): disable cuda graph execution if hicache loading triggered
new_batch.hicache_consumer_index = (
self.tree_cache.ready_to_load_host_cache()
)
new_batch.prepare_for_extend()
# Mixed-style chunked prefill
if (
self.is_mixed_chunk
and not self.running_batch.is_empty()
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(v1_spec_info_filtered=True)
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 = ScheduleBatch(
reqs=[], batch_is_full=self.running_batch.batch_is_full
)
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(v1_spec_info_filtered=True)
if batch.is_empty():
batch.batch_is_full = False
return batch
# Check if decode out of memory
if not batch.check_decode_mem(self.decode_mem_cache_buf_multiplier) or (
TEST_RETRACT and self.forward_ct % TEST_RETRACT_INTERVAL == 0
):
old_ratio = self.new_token_ratio
retracted_reqs, new_token_ratio, reqs_to_abort = batch.retract_decode(
self.server_args, self.decode_mem_cache_buf_multiplier
)
self.num_retracted_reqs = len(retracted_reqs)
if self.enable_metrics and (x := len(retracted_reqs)) > 0:
self.metrics_collector.increment_num_retracted_reqs(x)
self.new_token_ratio = new_token_ratio
for req in reqs_to_abort:
abort_reason: FINISH_ABORT = req.to_finish
self.send_to_tokenizer.send_output(
AbortReq(abort_message=abort_reason.message, rid=req.rid), req
)
logger.info(
"KV cache pool is full. Retract requests. "
f"#retracted_reqs: {len(retracted_reqs)}, "
f"#new_token_ratio: {old_ratio:.4f} -> {new_token_ratio:.4f}"
)
for req in retracted_reqs:
self._add_request_to_queue(req, is_retracted=True)
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:
batch.batch_is_full = False
# Update batch tensors
batch.prepare_for_decode()
return batch
def record_batch_in_overlap(self, model_worker_batch: ModelWorkerBatch):
# FIXME(lsyin): hacky way to keep a reference to avoid GPU tensors being freed by torch GC
# NOTE: More Reliable: record all tensors into the forward stream
# NOTE: - for all future tensors, we shall always read from future map
# - for all non-future tensors (produced only by schedule stream),
# we shall keep its reference not being release during all the forwarding pass
self.batch_record_ct = (self.batch_record_ct + 1) % 2
self.batch_record_buf[self.batch_record_ct] = model_worker_batch
def run_batch(
self,
batch: ScheduleBatch,
pp_proxy_tensors: Optional[PPProxyTensors] = None,
) -> Union[GenerationBatchResult, EmbeddingBatchResult]:
"""Run a batch."""
self.forward_ct += 1
# Whether to run the profiler
self._profile_batch_predicate(batch)
if self.forward_sleep_time is not None:
logger.info(f"Scheduler.run_batch sleep {self.forward_sleep_time}s")
time.sleep(self.forward_sleep_time)
# Capture prefill start time for EXTEND mode
if batch.forward_mode == ForwardMode.EXTEND:
current_time = time.perf_counter()
for req in batch.reqs:
req.time_stats.prefill_start_time_host = current_time
# Place holder handling for pd-disagg decode event loop
if batch.forward_mode.is_prebuilt():
return self._run_batch_prebuilt(batch)
# Run forward
if self.is_generation:
if self.spec_algorithm.is_none() or self.enable_overlap:
# In most cases, we use the model worker batch to run the forward.
worker_batch_or_batch = batch.get_model_worker_batch()
else:
# In speculative decoding v1 (non-overlap) case, we use the batch directly.
# TODO(lsyin): delete this branch after unifying the abstraction.
worker_batch_or_batch = batch
if self.enable_overlap:
model_worker_batch = worker_batch_or_batch
self.record_batch_in_overlap(model_worker_batch)
# Sampling info will be modified during forward, so we store a copy.
model_worker_batch.sampling_info = (
model_worker_batch.sampling_info.copy_for_forward()
)
bs = len(model_worker_batch.seq_lens)
future_indices = self.future_map.alloc_future_indices(bs)
with self.forward_stream_ctx:
self.forward_stream.wait_stream(self.default_stream)
self.future_map.resolve_future(model_worker_batch)
with self.record_forward_metrics(batch):
batch_result = self.model_worker.forward_batch_generation(
model_worker_batch
# here pp is not compatible with overlap
)
# FIXME(lsyin): maybe move this to forward_batch_generation
batch_result.copy_done = self.device_module.Event()
if batch_result.delay_sample_func is None:
self.future_map.store_to_map(future_indices, batch_result)
batch_result.copy_to_cpu(return_logprob=batch.return_logprob)
else:
batch_result.future_indices = future_indices
# FIXME(lsyin): move this assignment elsewhere
future_indices_or_next_token_ids = -future_indices.indices
if batch.is_eagle_v2:
# FIXME(lsyin): tmp code for eagle v2
# We only keep future indices for next draft input
batch.spec_info = batch_result.next_draft_input
batch.spec_info.future_indices = future_indices
# batch.spec_info = EagleDraftInput(
# future_indices=future_indices,
# verify_done=batch_result.next_draft_input.verify_done,
# )
# The future value, usually for next batch preparation
# Current implementation strictly synchronizes the seq_lens
batch.seq_lens = batch_result.next_draft_input.new_seq_lens
elif self.enable_pdmux and batch.forward_mode.is_split_prefill():
batch_result = self.tp_worker.forward_batch_split_prefill(batch)
future_indices_or_next_token_ids = batch_result.next_token_ids
else:
kwargs = (
{"pp_proxy_tensors": pp_proxy_tensors}
if self.spec_algorithm.is_none()
else {}
)
with self.record_forward_metrics(batch):
batch_result = self.model_worker.forward_batch_generation(
worker_batch_or_batch, **kwargs
)
future_indices_or_next_token_ids = batch_result.next_token_ids
self.update_cache_from_scheduler(batch, batch_result)
# NOTE: future_indices_or_next_token_ids is used in ScheduleBatch,
# which can probably be replaced by future_indices later [TODO(lsyin)].
# we shall still keep the original outputs, e.g. next_token_ids
# in the GenerationBatchOutput for processing after copy_done.
batch.output_ids = future_indices_or_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 or self.spec_algorithm.is_eagle():
batch_result.extend_input_len_per_req = [
req.extend_input_len for req in batch.reqs
]
batch_result.extend_logprob_start_len_per_req = [
req.extend_logprob_start_len for req in batch.reqs
]
else:
batch_result.extend_input_len_per_req = None
batch_result.extend_logprob_start_len_per_req = None
ret = batch_result
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)
# Capture prefill end time for EXTEND mode
if batch.forward_mode == ForwardMode.EXTEND:
current_time = time.perf_counter()
for req in batch.reqs:
req.time_stats.prefill_end_time_host = current_time
return ret
def launch_batch_sample_if_needed(
self, batch_result: GenerationBatchResult
) -> Union[GenerationBatchResult, EmbeddingBatchResult]:
# TODO(lsyin): make the delayed sample a default behavior after
# unifying the forward_batch_generation interface (related to spec V2).
if batch_result is None or batch_result.delay_sample_func is None:
return
with self.forward_stream_ctx:
self.forward_stream.wait_stream(self.default_stream)
_batch_result = batch_result.delay_sample_func()
assert _batch_result is batch_result
self.future_map.store_to_map(batch_result.future_indices, batch_result)
batch_result.copy_to_cpu(return_logprob=self.cur_batch.return_logprob)
def process_batch_result(
self,
batch: ScheduleBatch,
result: Union[GenerationBatchResult, EmbeddingBatchResult],
):
if batch.forward_mode.is_decode():
self.process_batch_result_decode(batch, result)
trace_slice_batch(RequestStage.DECODE_LOOP, batch.reqs)
elif batch.forward_mode.is_extend():
if batch.is_dllm():
self.process_batch_result_dllm(batch, result)
else:
self.process_batch_result_prefill(batch, result)
elif batch.forward_mode.is_prebuilt():
self.process_batch_result_prebuilt(batch)
elif batch.forward_mode.is_idle():
if self.enable_overlap:
if result.copy_done is not None:
result.copy_done.synchronize()
self.log_batch_result_stats(batch, result)
self.maybe_send_health_check_signal()
def maybe_send_health_check_signal(self):
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_output(HealthCheckOutput())
def move_ready_grammar_requests(self):
"""Move requests whose grammar objects are ready from grammar_queue to waiting_queue."""
num_ready_reqs = 0
num_timeout_reqs = 0
for req in self.grammar_queue:
try:
if req.finished(): # It is aborted by AbortReq
num_ready_reqs += 1
continue
req.grammar = req.grammar.result(timeout=0.03)
self.grammar_backend.set_cache(req.grammar_key, req.grammar.copy())
if req.grammar is INVALID_GRAMMAR_OBJ:
error_msg = f"Invalid grammar request: {req.grammar_key=}"
req.set_finish_with_abort(error_msg)
num_ready_reqs += 1
except futures._base.TimeoutError:
req.grammar_wait_ct += 1
# NOTE(lianmin): this timeout is the waiting time of the above line. It is
# not the waiting time from it enters the grammar queue.
if req.grammar_wait_ct > GRAMMAR_TIMEOUT / 0.03:
num_timeout_reqs = 1
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, num_timeout_reqs], dtype=torch.int32)
torch.distributed.all_reduce(
tensor, op=torch.distributed.ReduceOp.MAX, group=tp_group
)
num_ready_reqs_max, num_timeout_reqs_max = tensor.tolist()
for i in range(num_ready_reqs, num_ready_reqs_max):
req = self.grammar_queue[i]
if req.finished(): # It is aborted by AbortReq
continue
req.grammar = req.grammar.result()
self.grammar_backend.set_cache(req.grammar_key, req.grammar.copy())
if req.grammar is INVALID_GRAMMAR_OBJ:
error_msg = f"Invalid grammar request: {req.grammar_key=}"
req.set_finish_with_abort(error_msg)
else:
num_ready_reqs_max = num_ready_reqs
num_timeout_reqs_max = num_timeout_reqs
for i in range(num_ready_reqs, num_ready_reqs + num_timeout_reqs_max):
req = self.grammar_queue[i]
req.grammar.cancel()
self.grammar_backend.set_cache(req.grammar_key, INVALID_GRAMMAR_OBJ)
error_msg = f"Grammar preprocessing timed out for {req.grammar_key=}"
req.set_finish_with_abort(error_msg)
num_ready_reqs = num_ready_reqs_max + num_timeout_reqs_max
for req in self.grammar_queue[:num_ready_reqs]:
self._add_request_to_queue(req)
self.grammar_queue = self.grammar_queue[num_ready_reqs:]
def flush_cache_wrapped(self, recv_req: FlushCacheReqInput):
success = self.flush_cache()
return FlushCacheReqOutput(success=success)
def clear_hicache_storage_wrapped(self, recv_req: ClearHiCacheReqInput):
if self.enable_hierarchical_cache:
self.tree_cache.clear_storage_backend()
logger.info("Hierarchical cache cleared successfully!")
if_success = True
else:
logging.warning("Hierarchical cache is not enabled.")
if_success = False
return ClearHiCacheReqOutput(success=if_success)
def _is_no_request(self):
no_request = (
self.running_batch.is_empty()
and (self.last_batch is None or self.last_batch.is_empty())
and (self.cur_batch is None or self.cur_batch.is_empty())
and (not self.enable_overlap or len(self.result_queue) == 0)
and (self.pp_size == 1 or all(x.is_empty() for x in self.running_mbs))
)
if self.disaggregation_mode == DisaggregationMode.PREFILL:
no_request &= (
len(self.disagg_prefill_bootstrap_queue.queue) == 0
and len(self.disagg_prefill_inflight_queue) == 0
)
if self.disaggregation_mode == DisaggregationMode.DECODE:
no_request &= (
len(self.disagg_decode_prealloc_queue.queue) == 0
and len(self.disagg_decode_transfer_queue.queue) == 0
)
return no_request
def flush_cache(self):
"""Flush the memory pool and cache."""
if self._is_no_request():
self.cur_batch = None
self.last_batch = None
self.tree_cache.reset()
self.req_to_token_pool.clear()
self.token_to_kv_pool_allocator.clear()
if self.grammar_backend:
self.grammar_backend.reset()
self.reset_metrics()
if self.draft_worker:
self.draft_worker.clear_cache_pool()
# TODO: allow optional empty cache
torch.cuda.empty_cache()
logger.info("Cache flushed successfully!")
success = True
else:
logging.warning(
f"Cache not flushed because there are pending requests. "
f"#queue-req: {len(self.waiting_queue)}, "
f"#running-req: {len(self.running_batch.reqs)}"
)
success = False
return success
def get_internal_state(self, recv_req: GetInternalStateReq):
ret = vars(get_global_server_args())
ret["last_gen_throughput"] = self.last_gen_throughput
ret["memory_usage"] = {
"weight": round(self.tp_worker.model_runner.weight_load_mem_usage, 2),
"kvcache": round(
self.token_to_kv_pool_allocator.get_kvcache().mem_usage, 2
),
"token_capacity": int(self.max_total_num_tokens),
"graph": round(self.tp_worker.model_runner.graph_mem_usage, 2),
}
if not self.spec_algorithm.is_none() and self.spec_total_num_forward_ct > 0:
ret["avg_spec_accept_length"] = (
self.spec_total_num_accepted_tokens / self.spec_total_num_forward_ct
)
if RECORD_STEP_TIME:
ret["step_time_dict"] = self.step_time_dict
# This field is not serializable.
ret.pop("model_config", None)
return GetInternalStateReqOutput(internal_state=ret)
def set_internal_state(self, recv_req: SetInternalStateReq):
server_args_dict = recv_req.server_args
args_allow_update = set(
[
"pp_max_micro_batch_size",
"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
elif k == "pp_max_micro_batch_size" and (
v > self.max_running_requests // self.pp_size or v < 1
):
logging.warning(
f"Updating {k} to {v} is rejected because it is out of the valid range [1, {self.max_running_requests // self.pp_size}]."
)
if_success = False
break
if if_success:
if not self.spec_algorithm.is_none() and self.spec_total_num_forward_ct > 0:
avg_spec_accept_length = (
self.spec_total_num_accepted_tokens / self.spec_total_num_forward_ct
)
logger.info(f"{avg_spec_accept_length=}")
self.spec_total_num_accepted_tokens = self.spec_total_num_forward_ct = 0
for k, v in server_args_dict.items():
setattr(get_global_server_args(), k, v)
logger.info(f"Global server args updated! {get_global_server_args()=}")
return SetInternalStateReqOutput(
updated=True,
server_args=vars(get_global_server_args()),
)
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)
if recv_req.parameters is not None:
func(**recv_req.parameters)
else:
func()
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 abort_request(self, recv_req: AbortReq):
# Delete requests in the waiting queue
to_del = []
for i, req in enumerate(self.waiting_queue):
if recv_req.abort_all or req.rid.startswith(recv_req.rid):
to_del.append(i)
# Sort in reverse order to avoid index issues when deleting
for i in reversed(to_del):
# Abort method 1: directly pop from the queue
# This only works for requests that have not started anything.
# We still need to send something back to TokenizerManager to clean up the state.
req = self.waiting_queue.pop(i)
if self.enable_hicache_storage:
# to release prefetch events associated with the request
self.tree_cache.release_aborted_request(req.rid)
self.send_to_tokenizer.send_output(AbortReq(rid=req.rid), req)
# For disaggregation decode mode, the request in the waiting queue has KV cache allocated.
if self.disaggregation_mode == DisaggregationMode.DECODE:
release_kv_cache(req, self.tree_cache)
# For mamba radix cache
if req.mamba_pool_idx is not None:
release_kv_cache(req, self.tree_cache, is_insert=False)
logger.debug(f"Abort queued request. {req.rid=}")
# Delete the requests in the grammar queue
for req in self.grammar_queue:
# Abort method 2: call `set_finish_with_abort`
# The request will still run one prefill forward pass.
# In this case, we change the input_ids to be only one token to make this prefill cheap.
if recv_req.abort_all or req.rid.startswith(recv_req.rid):
logger.debug(f"Abort grammar queue request. {req.rid=}")
if req.grammar:
req.grammar.cancel()
req.set_finish_with_abort("Aborted by AbortReq.")
# Delete requests not in the waiting queue when PD disaggregation is enabled
if self.disaggregation_mode == DisaggregationMode.PREFILL:
# Abort requests that have not yet been bootstrapped
for req in self.disagg_prefill_bootstrap_queue.queue:
if recv_req.abort_all or req.rid.startswith(recv_req.rid):
logger.debug(f"Abort bootstrap queue request. {req.rid=}")
if hasattr(req.disagg_kv_sender, "abort"):
req.disagg_kv_sender.abort()
# Abort in-flight requests
for req in self.disagg_prefill_inflight_queue:
if recv_req.abort_all or req.rid.startswith(recv_req.rid):
logger.debug(f"Abort inflight queue request. {req.rid=}")
if hasattr(req.disagg_kv_sender, "abort"):
req.disagg_kv_sender.abort()
elif self.disaggregation_mode == DisaggregationMode.DECODE:
# Abort requests that have not yet finished preallocation
for decode_req in self.disagg_decode_prealloc_queue.queue:
if recv_req.abort_all or decode_req.req.rid.startswith(recv_req.rid):
logger.debug(f"Abort prealloc queue request. {decode_req.req.rid=}")
decode_req.kv_receiver.abort()
# Abort requests waiting for kvcache to release tree cache
for decode_req in self.disagg_decode_transfer_queue.queue:
if recv_req.abort_all or decode_req.req.rid.startswith(recv_req.rid):
logger.debug(f"Abort transfer queue request. {decode_req.req.rid=}")
decode_req.kv_receiver.abort()
# Delete requests in the running batch
if self.cur_batch is self.running_batch or self.cur_batch is None:
reqs = self.running_batch.reqs
else:
reqs = self.running_batch.reqs + self.cur_batch.reqs
for req in reqs:
if not req.finished() and (
recv_req.abort_all or req.rid.startswith(recv_req.rid)
):
# Abort method 3: set `to_finish`
# The request will still run one decode forward pass.
# Then we reuse all existing code to clean up the KV cache allocation.
logger.debug(f"Abort running request. {req.rid=}")
req.to_finish = FINISH_ABORT()
def _pause_engine(self) -> Tuple[List[Req], int]:
raise NotImplementedError()
def pause_generation(self, recv_req: PauseGenerationReqInput):
self._engine_paused = True
if self.enable_overlap and self.last_batch:
# Process the results of the last batch
tmp_batch, tmp_result = self.result_queue.popleft()
self.process_batch_result(tmp_batch, tmp_result)
if self.last_batch and self.last_batch.forward_mode.is_extend():
chunked_req_to_exclude = set()
if recv_req.mode == "in_place":
if self.chunked_req is not None:
chunked_req_to_exclude.add(self.chunked_req)
self.last_batch.filter_batch(
chunked_req_to_exclude=list(chunked_req_to_exclude)
)
self.running_batch.merge_batch(self.last_batch)
self.last_batch = None
self.cur_batch = None
if recv_req.mode == "retract":
self.running_batch.filter_batch(v1_spec_info_filtered=True)
if len(self.running_batch.reqs) != 0:
retracted_reqs = self.running_batch.retract_all(self.server_args)
for req in retracted_reqs:
self._add_request_to_queue(req)
self.running_batch.batch_is_full = False
self.chunked_req = None
def continue_generation(self, recv_req: ContinueGenerationReqInput):
self._engine_paused = False
def load_lora_adapter(
self, recv_req: LoadLoRAAdapterReqInput
) -> LoadLoRAAdapterReqOutput:
"""In-place loading a new lora adapter from disk or huggingface."""
result = self.tp_worker.load_lora_adapter(recv_req)
return result
def unload_lora_adapter(
self, recv_req: UnloadLoRAAdapterReqInput
) -> UnloadLoRAAdapterReqOutput:
"""Unload the lora adapter."""
result = self.tp_worker.unload_lora_adapter(recv_req)
return result
def init_weights_send_group_for_remote_instance(
self, recv_req: InitWeightsSendGroupForRemoteInstanceReqInput
):
"""Init the seed and client instance communication group."""
success, message = self.tp_worker.init_weights_send_group_for_remote_instance(
recv_req
)
return InitWeightsSendGroupForRemoteInstanceReqOutput(success, message)
def send_weights_to_remote_instance(
self, recv_req: SendWeightsToRemoteInstanceReqInput
):
"""Send the seed instance weights to the destination instance."""
success, message = self.tp_worker.send_weights_to_remote_instance(recv_req)
return SendWeightsToRemoteInstanceReqOutput(success, message)
def slow_down(self, recv_req: SlowDownReqInput):
t = recv_req.forward_sleep_time
if t is not None and t <= 0:
t = None
self.forward_sleep_time = t
return SlowDownReqOutput()
def expert_distribution_handle(self, recv_req: ExpertDistributionReq):
action = recv_req.action
if action == ExpertDistributionReqType.START_RECORD:
get_global_expert_distribution_recorder().start_record()
elif action == ExpertDistributionReqType.STOP_RECORD:
get_global_expert_distribution_recorder().stop_record()
elif action == ExpertDistributionReqType.DUMP_RECORD:
get_global_expert_distribution_recorder().dump_record()
else:
raise ValueError(f"Unrecognized ExpertDistributionReq value: {recv_req=}")
return ExpertDistributionReqOutput()
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 maybe_sleep_on_idle(self):
if self.idle_sleeper is not None:
self.idle_sleeper.maybe_sleep()
def handle_freeze_gc(self, recv_req: FreezeGCReq):
"""Handle freeze_gc request: freeze scheduler's GC and forward to detokenizer."""
freeze_gc("Scheduler")
self.send_to_detokenizer.send_output(recv_req, recv_req)
return None
# placeholder for override
def update_cache_from_scheduler(
self, schedule_batch: ScheduleBatch, batch_result: GenerationBatchResult
):
pass
def get_remote_instance_transfer_engine_info(self):
return self.tp_worker.get_remote_instance_transfer_engine_info()
class IdleSleeper:
"""
In setups which have long inactivity periods it is desirable to reduce
system power consumption when sglang does nothing. This would lead not only
to power savings, but also to more CPU thermal headroom when a request
eventually comes. This is important in cases when multiple GPUs are connected
as each GPU would otherwise pin one thread at 100% CPU usage.
The simplest solution is to use zmq.Poller on all sockets that may receive
data that needs handling immediately.
"""
def __init__(self, sockets):
self.poller = zmq.Poller()
self.last_empty_time = time.time()
for s in sockets:
self.poller.register(s, zmq.POLLIN)
self.empty_cache_interval = envs.SGLANG_EMPTY_CACHE_INTERVAL.get()
def maybe_sleep(self):
self.poller.poll(1000)
if (
self.empty_cache_interval > 0
and time.time() - self.last_empty_time > self.empty_cache_interval
):
self.last_empty_time = time.time()
torch.cuda.empty_cache()
def is_health_check_generate_req(recv_req):
rid = getattr(recv_req, "rid", None)
return rid is not None and rid.startswith("HEALTH_CHECK")
def is_work_request(recv_req):
return isinstance(
recv_req,
(
TokenizedGenerateReqInput,
TokenizedEmbeddingReqInput,
BatchTokenizedGenerateReqInput,
BatchTokenizedEmbeddingReqInput,
),
)
class SenderWrapper:
def __init__(self, socket: zmq.Socket):
self.socket = socket
def send_output(
self,
output: Union[BaseReq, BaseBatchReq],
recv_obj: Optional[Union[BaseReq, BaseBatchReq]] = None,
):
if self.socket is None:
return
if (
isinstance(recv_obj, BaseReq)
and recv_obj.http_worker_ipc is not None
and output.http_worker_ipc is None
):
# handle communicator reqs for multi-http worker case
output.http_worker_ipc = recv_obj.http_worker_ipc
self.socket.send_pyobj(output)
def run_scheduler_process(
server_args: ServerArgs,
port_args: PortArgs,
gpu_id: int,
tp_rank: int,
moe_ep_rank: int,
pp_rank: int,
dp_rank: Optional[int],
pipe_writer,
):
# Generate the logger prefix
prefix = ""
if dp_rank is None and "SGLANG_DP_RANK" in os.environ:
# [For Router] if env var "SGLANG_DP_RANK" exist, set dp_rank to the value of the env var
dp_rank = int(os.environ["SGLANG_DP_RANK"])
if dp_rank is not None:
prefix += f" DP{dp_rank}"
if server_args.pp_size > 1:
prefix += f" PP{pp_rank}"
if server_args.tp_size > 1:
prefix += f" TP{tp_rank}"
if server_args.ep_size > 1:
prefix += f" EP{moe_ep_rank}"
# Config the process
setproctitle.setproctitle(f"sglang::scheduler{prefix.replace(' ', '_')}")
faulthandler.enable()
kill_itself_when_parent_died()
parent_process = psutil.Process().parent()
# Configure the logger
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.pp_size, server_args.tp_size, server_args.nnodes, gpu_id
)
if (
numa_node := server_args.numa_node
) is not None and not envs.SGLANG_NUMA_BIND_V2.get():
numa_bind_to_node(numa_node[gpu_id])
# Set up tracing
if server_args.enable_trace:
process_tracing_init(server_args.otlp_traces_endpoint, "sglang")
thread_label = "Scheduler"
if server_args.disaggregation_mode == "prefill":
thread_label = "Prefill Scheduler"
elif server_args.disaggregation_mode == "decode":
thread_label = "Decode Scheduler"
trace_set_thread_info(thread_label, tp_rank, dp_rank)
# Create a scheduler and run the event loop
try:
scheduler = Scheduler(
server_args,
port_args,
gpu_id,
tp_rank,
moe_ep_rank,
pp_rank,
dp_rank,
)
result_dict = {
"status": "ready",
"max_total_num_tokens": scheduler.max_total_num_tokens,
"max_req_input_len": scheduler.max_req_input_len,
}
if server_args.remote_instance_weight_loader_use_transfer_engine():
(
remote_instance_transfer_engine_session_id,
remote_instance_transfer_engine_weights_info_dict,
) = scheduler.get_remote_instance_transfer_engine_info()
result_dict.update(
{
"tp_rank": tp_rank,
"remote_instance_transfer_engine_session_id": remote_instance_transfer_engine_session_id,
"remote_instance_transfer_engine_weights_info_dict": remote_instance_transfer_engine_weights_info_dict,
}
)
pipe_writer.send(result_dict)
# Dispatch to the appropriate event loop based on the disaggregation mode
disaggregation_mode: DisaggregationMode = scheduler.disaggregation_mode
if disaggregation_mode == DisaggregationMode.NULL:
if scheduler.enable_pdmux:
scheduler.event_loop_pdmux()
elif server_args.pp_size > 1:
scheduler.event_loop_pp()
elif scheduler.enable_overlap:
scheduler.event_loop_overlap()
else:
scheduler.event_loop_normal()
elif disaggregation_mode == DisaggregationMode.PREFILL:
if server_args.pp_size > 1:
scheduler.event_loop_pp_disagg_prefill()
elif scheduler.enable_overlap:
scheduler.event_loop_overlap_disagg_prefill()
else:
scheduler.event_loop_normal_disagg_prefill()
elif disaggregation_mode == DisaggregationMode.DECODE:
if server_args.pp_size > 1:
scheduler.event_loop_pp_disagg_decode()
elif scheduler.enable_overlap:
scheduler.event_loop_overlap_disagg_decode()
else:
scheduler.event_loop_normal_disagg_decode()
except Exception:
traceback = get_exception_traceback()
logger.error(f"Scheduler hit an exception: {traceback}")
parent_process.send_signal(signal.SIGQUIT)
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