chenchenghao/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity
Files
e9c341afe8e90b309c390101a79b3c62988e2499
sglang/python/sglang/srt/managers/scheduler.py
laoyao0822 e9c341afe8 Hold decode EAGLE metadata until prebuilt consumption 
Transferred EAGLE metadata buffers are reusable slot views. The previous clone-based mitigation protected correctness but added copies on the transfer hot path and hid the actual lifetime contract. This change makes the transferred request own the metadata slot while it sits in the decode waiting queue, then releases it immediately after process_prebuilt has consumed top-k and hidden state into the prebuilt batch. Abort paths also release any held decode metadata slot.

Constraint: Decode disaggregation metadata buffers are reusable slot views consumed later by process_prebuilt.

Rejected: Clone transferred EAGLE tensors at commit time | correct but less efficient and masks the ownership contract.

Rejected: Release in process_batch_result_prebuilt | holds slots across forward longer than needed.

Confidence: medium

Scope-risk: moderate

Directive: Do not free successful EAGLE transfer metadata in pop_transferred unless process_prebuilt consumption is also moved earlier.

Tested: Remote py_compile for decode.py, scheduler.py, scheduler_output_processor_mixin.py, and test_decode_queue_compaction.py.

Tested: Remote focused lifecycle tests passed: 3 passed.

Tested: Remote full test_decode_queue_compaction.py passed: 11 passed, 5 warnings.

Not-tested: Fresh ETE runtime validation of EAGLE accept-length recovery after the C48 sync.
2026-05-30 04:51:50 +08:00

3635 lines
148 KiB
Python
Raw Blame History

# 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 contextlib import nullcontext
from dataclasses import dataclass
from http import HTTPStatus
from typing import Any, Deque, Dict, List, Optional, Tuple, Union
from sglang.srt.utils.common import suppress_noisy_warnings
suppress_noisy_warnings()
import psutil
import setproctitle
import torch
import torch.distributed
import zmq
from torch.cuda import Stream as CudaStream
from torch.distributed import barrier
from sglang.jit_kernel.ngram_embedding import update_token_table
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.constrained.grammar_manager import GrammarManager
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 create_mm_receiver
from sglang.srt.disaggregation.prefill import (
PrefillBootstrapQueue,
SchedulerDisaggregationPrefillMixin,
release_req_to_metadata_buffer,
)
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.distributed.parallel_state import get_tp_group
from sglang.srt.dllm.mixin.scheduler import SchedulerDllmMixin
from sglang.srt.environ import envs
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.layers.attention.mamba.ops import (
initialize_mamba_selective_state_update_backend,
)
from sglang.srt.layers.dp_attention import (
compute_dp_attention_world_info,
get_attention_cp_group,
get_attention_tp_group,
)
from sglang.srt.layers.moe import initialize_moe_config
from sglang.srt.layers.quantization.fp4_utils import initialize_fp4_gemm_config
from sglang.srt.layers.quantization.fp8_utils import initialize_fp8_gemm_config
from sglang.srt.lora.lora_overlap_loader import LoRAOverlapLoader
from sglang.srt.managers.hisparse_coordinator import HiSparseCoordinator
from sglang.srt.managers.io_struct import (
AbortReq,
ActiveRanksOutput,
AttachHiCacheStorageReqInput,
AttachHiCacheStorageReqOutput,
BaseBatchReq,
BaseReq,
BatchTokenizedEmbeddingReqInput,
BatchTokenizedGenerateReqInput,
CheckWeightsReqInput,
ClearHiCacheReqInput,
ClearHiCacheReqOutput,
CloseSessionReqInput,
ContinueGenerationReqInput,
DestroyWeightsUpdateGroupReqInput,
DetachHiCacheStorageReqInput,
DetachHiCacheStorageReqOutput,
DumperControlReqInput,
DumperControlReqOutput,
ExpertDistributionReq,
ExpertDistributionReqOutput,
ExpertDistributionReqType,
FlushCacheReqInput,
FlushCacheReqOutput,
FreezeGCReq,
GetInternalStateReq,
GetInternalStateReqOutput,
GetLoadReqInput,
GetLoadsReqInput,
GetWeightsByNameReqInput,
HealthCheckOutput,
InitWeightsSendGroupForRemoteInstanceReqInput,
InitWeightsSendGroupForRemoteInstanceReqOutput,
InitWeightsUpdateGroupReqInput,
LoadLoRAAdapterFromTensorsReqInput,
LoadLoRAAdapterFromTensorsReqOutput,
LoadLoRAAdapterReqInput,
LoadLoRAAdapterReqOutput,
OpenSessionReqInput,
PauseGenerationReqInput,
PinPrefixReqInput,
PinPrefixReqOutput,
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, unwrap_shm_features
from sglang.srt.managers.overlap_utils import FutureMap
from sglang.srt.managers.prefill_delayer import (
PrefillDelayer,
PrefillDelayerSinglePassExecutor,
)
from sglang.srt.managers.schedule_batch import (
FINISH_ABORT,
ModelWorkerBatch,
MultimodalInputs,
Req,
ScheduleBatch,
)
from sglang.srt.managers.scheduler_graceful_shutdown_mixin import (
SchedulerGracefulShutdownMixin,
)
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_input_blocker import SchedulerInputBlocker
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 SessionController
from sglang.srt.managers.utils import GenerationBatchResult, validate_input_length
from sglang.srt.mem_cache.base_prefix_cache import DecLockRefParams
from sglang.srt.mem_cache.cache_init_params import CacheInitParams
from sglang.srt.mem_cache.common import KVCapacityWaitError, release_kv_cache
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.mem_cache.session_aware_cache import SessionAwareCache
from sglang.srt.model_executor.forward_batch_info import ForwardMode, PPProxyTensors
from sglang.srt.multiplex.multiplexing_mixin import SchedulerMultiplexMixin
from sglang.srt.observability.req_time_stats import (
real_time,
set_schedule_time_batch,
set_time_batch,
)
from sglang.srt.observability.scheduler_metrics_mixin import (
RECORD_STEP_TIME,
PrefillStats,
SchedulerMetricsMixin,
)
from sglang.srt.observability.trace import process_tracing_init, trace_set_thread_info
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
from sglang.srt.server_args import PortArgs, ServerArgs, get_global_server_args
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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_numa_node,
is_mps,
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.common import is_npu
from sglang.srt.utils.hf_transformers_utils import (
get_processor,
get_tokenizer,
get_tokenizer_from_processor,
)
from sglang.srt.utils.network import get_zmq_socket
from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
from sglang.utils import TypeBasedDispatcher, get_exception_traceback
if is_mps():
CudaStreamContext = nullcontext
else:
from torch.cuda import StreamContext as CudaStreamContext
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()
def _cp_draft_pool_summary(pool) -> str:
if pool is None:
return "None"
parts = [pool.__class__.__name__]
for attr in ("size", "page_size", "start_layer", "end_layer", "layer_num"):
if hasattr(pool, attr):
parts.append(f"{attr}={getattr(pool, attr)}")
return " ".join(parts)
def _cp_draft_shared_kv_debug(message: str, *args) -> None:
if envs.SGLANG_CP_DRAFT_SHARED_KV_DEBUG.get():
logger.info("[CP_DRAFT_SHARED_KV] " + message, *args)
_is_npu = is_npu()
@dataclass
class EmbeddingBatchResult:
embeddings: torch.Tensor
copy_done: Optional[torch.cuda.Event] = None
def copy_to_cpu(self):
"""Copy embeddings tensor to CPU in overlap scheduling."""
if isinstance(self.embeddings, torch.Tensor):
self.copy_done = torch.get_device_module(self.embeddings.device).Event()
self.embeddings = self.embeddings.to("cpu", non_blocking=True)
else:
assert isinstance(self.embeddings, list)
if len(self.embeddings) == 0:
return
self.copy_done = torch.get_device_module(self.embeddings[0].device).Event()
self.embeddings = [
emb.to("cpu", non_blocking=True) for emb in self.embeddings
]
self.copy_done.record()
class Scheduler(
SchedulerOutputProcessorMixin,
SchedulerUpdateWeightsMixin,
SchedulerProfilerMixin,
SchedulerMetricsMixin,
SchedulerDisaggregationDecodeMixin,
SchedulerDisaggregationPrefillMixin,
SchedulerMultiplexMixin,
SchedulerRuntimeCheckerMixin,
SchedulerPPMixin,
SchedulerDPAttnMixin,
SchedulerGracefulShutdownMixin,
SchedulerDllmMixin,
):
"""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,
attn_cp_rank: int,
moe_dp_rank: int,
dp_rank: Optional[int],
):
self.is_initializing = True
self.init_soft_watchdog(server_args)
# 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.attn_cp_rank = attn_cp_rank
self.attn_cp_size = server_args.attn_cp_size
self.moe_dp_rank = moe_dp_rank
self.moe_dp_size = server_args.moe_dp_size
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.enable_lora_overlap_loading = server_args.enable_lora_overlap_loading
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.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()
self.enable_hisparse = server_args.enable_hisparse
self.hisparse_coordinator: Optional[HiSparseCoordinator] = None
# 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,
self.attn_cp_size,
)
)
self.enable_kv_cache_events = bool(
server_args.kv_events_config and self.attn_tp_rank == 0
)
# 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_ipc_channels(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()
# Init mamba backend
self.init_mamba_backend()
# Launch a model worker and draft model worker if using speculative decoding
self.init_model_worker()
if (t := envs.SGLANG_TEST_STUCK_SCHEDULER_INIT.get()) > 0:
time.sleep(t)
# 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 diffusion LLM
self.init_diffusion_llm()
# 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 Ngram Embedding
self.maybe_init_ngram_embedding()
# 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()
# Init LoRA overlap loader
if self.enable_lora_overlap_loading:
self.lora_overlap_loader = LoRAOverlapLoader(
self.tp_worker.model_runner.lora_manager
)
# Init the grammar backend for constrained generation
self.grammar_manager = GrammarManager(self)
self.is_initializing = False
def init_model_config(self):
self.model_config = ModelConfig.from_server_args(self.server_args)
if _is_npu:
# make sure the page size is not larger than block_size and chunked_prefill_size on NPU backend
# the npu backend request the defined page size to be no larger than block_size and chunked_prefill_size
from sglang.srt.dllm.config import DllmConfig
self.dllm_config = ( # For diffusion LLM
DllmConfig.from_server_args(self.server_args)
if self.server_args.dllm_algorithm is not None
else None
)
if self.dllm_config:
if self.dllm_config.block_size < self.page_size:
logger.warning(
"WARNING: "
f"The page size {self.page_size} should not be larger than dllm block size {self.dllm_config.block_size}."
f"Page size now falls back to {self.dllm_config.block_size}"
)
self.page_size = self.dllm_config.block_size
def init_ipc_channels(self, port_args: PortArgs):
context = zmq.Context(2)
self.idle_sleeper = None
if self.pp_rank == 0 and self.attn_tp_rank == 0 and self.attn_cp_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 self.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_mamba_backend(self) -> None:
initialize_mamba_selective_state_update_backend(self.server_args)
def init_moe_gemm_config(self):
# For the MM models, check the text_config for MoE settings
config_to_check = getattr(
self.model_config.hf_config, "text_config", self.model_config.hf_config
)
if hasattr(config_to_check, "num_experts_per_tok"):
initialize_moe_config(self.server_args)
# Initialize GEMM-related configuration for FP8 and FP4 backends.
initialize_fp8_gemm_config(self.server_args)
initialize_fp4_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_tp_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,
attn_cp_rank=self.attn_cp_rank,
moe_dp_rank=self.moe_dp_rank,
dp_rank=self.dp_rank,
nccl_port=self.nccl_port,
)
def maybe_init_draft_worker(self):
if self.spec_algorithm.is_none():
self.draft_worker = None
return
# 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,
attn_cp_rank=self.attn_cp_rank,
moe_dp_rank=self.moe_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}'"
)
DraftWorkerClass = self.spec_algorithm.create_worker(self.server_args)
self.draft_worker = DraftWorkerClass(**draft_worker_kwargs)
def _get_draft_token_to_kv_pool_for_hicache(self):
if self.draft_worker is None or self.spec_algorithm.is_ngram():
return None
if self.spec_algorithm.supports_spec_v2() 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
return draft_runner.token_to_kv_pool
return self.draft_worker.model_runner.token_to_kv_pool
def init_model_worker(self):
self.init_tp_model_worker()
self.maybe_init_draft_worker()
# 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.forward_stream,
_,
_,
_,
) = 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 = get_tp_group()
self.tp_cpu_group = self.tp_group.cpu_group
self.attn_tp_group = get_attention_tp_group()
self.attn_tp_cpu_group = self.attn_tp_group.cpu_group
self.attn_cp_group = get_attention_cp_group()
self.attn_cp_cpu_group = self.attn_cp_group.cpu_group
self.pp_group = get_pp_group()
self.world_group = get_world_group()
# NOTE: dp_tp_* are request/data-plane coordination groups (not tensor collectives).
# When DP attention is enabled, scope to the attention-TP group; otherwise use
# the base TP group. Entry rank is the local rank 0 in that group.
# Use the CPU (gloo) group to broadcast VLM Python objects and avoid CUDA
# stream/device coupling (#11910).
self.dp_tp_group = (
self.attn_tp_group
if self.server_args.enable_dp_attention
else self.tp_group
)
self.dp_tp_cpu_group = self.dp_tp_group.cpu_group
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"
)
if self.enable_metrics and hasattr(self, "metrics_collector"):
self.metrics_collector.emit_cache_config_info(
self.page_size, self.max_total_num_tokens // self.page_size
)
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
)
self.sliding_window_size = 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(),
pp_rank=self.pp_rank,
pp_size=self.pp_size,
chunked_prefill_size=server_args.chunked_prefill_size,
sliding_window_size=self.sliding_window_size,
draft_token_to_kv_pool=self._get_draft_token_to_kv_pool_for_hicache(),
)
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
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:
if self.is_hybrid_ssm:
from sglang.srt.mem_cache.hi_mamba_radix_cache import (
HiMambaRadixCache,
)
self.tree_cache = HiMambaRadixCache(
params=params, server_args=server_args
)
else:
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)
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.enable_streaming_session:
self.tree_cache = SessionAwareCache(self.tree_cache)
if self.enable_hisparse:
# Coordinator was created inside ModelRunner.initialize() before CUDA graph capture
self.hisparse_coordinator = self.tp_worker.model_runner.hisparse_coordinator
self.hisparse_coordinator.set_decode_producer_stream(self.forward_stream)
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
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 last forward batch
self.last_batch: Optional[ScheduleBatch] = None
self.forward_ct = 0
self.return_health_check_ipcs: Deque[Optional[str]] = deque()
self.num_retracted_reqs: int = 0
self.num_paused_reqs: int = 0
self.session_controller = SessionController(self.tree_cache)
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.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_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.prefill_delayer: Optional[PrefillDelayer] = None
self.max_prefill_bs: int = 0
if self.server_args.enable_prefill_delayer:
self.prefill_delayer = PrefillDelayer(
dp_size=self.dp_size,
attn_tp_size=self.attn_tp_size,
cpu_group=self.tp_cpu_group,
server_args=self.server_args,
metrics_collector=(
self.metrics_collector if self.enable_metrics else None
),
max_delay_passes=self.server_args.prefill_delayer_max_delay_passes,
token_usage_low_watermark=self.server_args.prefill_delayer_token_usage_low_watermark,
device=(
self.tp_group.device
if self.server_args.disable_overlap_schedule
else "cpu"
),
)
# NOTE: preemption is enabled by default for priority scheduling.
self.enable_priority_preemption = (
self.enable_priority_scheduling
and not self.server_args.disable_priority_preemption
)
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_soft_watchdog(self, server_args: ServerArgs):
if (x := server_args.soft_watchdog_timeout) is not None:
self.soft_watchdog = create_scheduler_watchdog(
self, watchdog_timeout=x, soft=True
)
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
)
# 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.supports_spec_v2() 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
_cp_draft_shared_kv_debug(
"scheduler_disagg_init mode=%s spec_algorithm=%s draft_worker=%s "
"draft_pool=(%s) target_pool=(%s)",
self.disaggregation_mode,
self.spec_algorithm,
self.draft_worker is not None,
_cp_draft_pool_summary(draft_token_to_kv_pool),
_cp_draft_pool_summary(self.token_to_kv_pool_allocator.get_kvcache()),
)
if draft_token_to_kv_pool is not None and hasattr(
self.tree_cache, "attach_draft_kv_pool"
):
self.tree_cache.attach_draft_kv_pool(draft_token_to_kv_pool)
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,
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()
or self.spec_algorithm.is_standalone()
else 16 # minimal padding size for RDMA
),
hidden_states_dtype=(
model_config.dtype
if self.spec_algorithm.is_eagle()
or self.spec_algorithm.is_standalone()
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,
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 = create_mm_receiver(
self.server_args,
hf_config=self.model_config.hf_config,
pp_rank=self.pp_rank,
tp_rank=self.tp_rank,
tp_group=self.tp_group,
scheduler=self,
)
def init_overlap(self):
self.device_module = torch.get_device_module(self.device)
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 maybe_init_ngram_embedding(self):
self.use_ngram_embedding = self.tp_worker.model_config.use_ngram_embedding
if self.use_ngram_embedding:
self.token_table = self.tp_worker.model_runner.token_table
hf_config = self.tp_worker.model_config.hf_config
self.ngram_embedding_n = hf_config.ngram_embedding_n
self.ngram_embedding_k = hf_config.ngram_embedding_k
def _maybe_prepare_ngram_embedding(
self, batch: Optional[ScheduleBatch]
) -> Optional[ScheduleBatch]:
"""Fill the token table for ngram embedding before a forward pass."""
if batch is None or not self.use_ngram_embedding:
return batch
batch.ne_token_table = self.token_table
if batch.forward_mode == ForwardMode.EXTEND:
all_tokens = []
column_starts = []
request_lengths = []
for req in batch.reqs:
start = len(req.prefix_indices)
end = start + req.extend_input_len
fill_ids = req.origin_input_ids + req.output_ids
if start == 0:
tokens = fill_ids[start:end]
column_starts.append(0)
elif start < self.ngram_embedding_n:
tokens = fill_ids[0:end]
column_starts.append(0)
else:
# Prepend n-1 tokens before prefix_len for n-gram context
tokens = fill_ids[start - self.ngram_embedding_n + 1 : end]
column_starts.append(start - self.ngram_embedding_n + 1)
all_tokens.extend(tokens)
request_lengths.append(len(tokens))
dtype = self.token_table.dtype
device = self.token_table.device
update_token_table(
ne_token_table=self.token_table,
tokens=torch.tensor(all_tokens, dtype=dtype, device=device),
row_indices=batch.req_pool_indices,
column_starts=torch.tensor(
column_starts, dtype=torch.int32, device=device
),
req_lens=torch.tensor(
request_lengths, dtype=torch.int32, device=device
),
ignore_tokens=None,
)
return batch
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),
(AttachHiCacheStorageReqInput, self.attach_hicache_storage_wrapped),
(DetachHiCacheStorageReqInput, self.detach_hicache_storage_wrapped),
(PinPrefixReqInput, self.pin_prefix_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),
(
LoadLoRAAdapterFromTensorsReqInput,
self.load_lora_adapter_from_tensors,
),
(UnloadLoRAAdapterReqInput, self.unload_lora_adapter),
(GetLoadReqInput, self.get_load),
(GetLoadsReqInput, self.get_loads),
(PauseGenerationReqInput, self.pause_generation),
(ContinueGenerationReqInput, self.continue_generation),
(DumperControlReqInput, self.handle_dumper_control),
]
)
def _abort_on_running_timeout(self):
# NOTE: this should be called before a batch is launched,
# as current spec-v1 still filters batch inside verify stage.
timeout_s = envs.SGLANG_REQ_RUNNING_TIMEOUT.get()
if timeout_s <= 0:
return
if self.running_batch.is_empty():
return
deadline = time.perf_counter() - timeout_s
for req in self.running_batch.reqs:
if not req.finished() and 0 < req.time_stats.forward_entry_time < deadline:
req.to_finish = FINISH_ABORT(
"Request running timeout reached.", HTTPStatus.SERVICE_UNAVAILABLE
)
def get_init_info(self) -> Dict[str, Any]:
"""Return scheduler initialization info for handshake.
This method provides the initialization info needed by the tokenizer manager
and other components to verify the scheduler is ready.
"""
result_dict = {
"status": "ready",
"max_total_num_tokens": self.max_total_num_tokens,
"max_req_input_len": self.max_req_input_len,
}
if self.server_args.remote_instance_weight_loader_use_transfer_engine():
(
remote_instance_transfer_engine_session_id,
remote_instance_transfer_engine_weights_info_dict,
) = self.get_remote_instance_transfer_engine_info()
result_dict.update(
{
"tp_rank": self.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,
}
)
return result_dict
def run_event_loop(self) -> None:
"""Run the scheduler's event loop.
Sets up the schedule stream and dispatches to the appropriate event loop.
The event loop blocks until shutdown.
"""
self.schedule_stream = self.device_module.Stream(priority=0)
if self.device == "cpu":
self.schedule_stream.synchronize = lambda: None # No-op for CPU
with self.device_module.StreamContext(self.schedule_stream):
dispatch_event_loop(self)
@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:
self.cancel_bubble_timer()
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, Union[GenerationBatchResult, EmbeddingBatchResult]]
] = 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
self.cancel_bubble_timer()
# 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.
if self.is_generation:
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.
# In DP attention mode, use the globally synchronized is_extend_in_batch
# so all DP ranks make the same overlap decision (avoiding deadlock).
# In non-DP mode, use the local forward_mode directly.
if self.require_mlp_sync:
is_extend = lambda b: b and b.is_extend_in_batch
else:
is_extend = lambda b: b and b.forward_mode.is_extend()
batch_is_extend = is_extend(batch)
last_batch_is_extend = is_extend(self.last_batch)
disable_overlap_for_batch = (
envs.SGLANG_DISABLE_CONSECUTIVE_PREFILL_OVERLAP.get()
and batch_is_extend
and last_batch_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_spec_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 and self.attn_cp_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)
recv_req = unwrap_shm_features(recv_req)
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 and self.attn_cp_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 and self.attn_cp_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.attn_cp_size != 1:
work_reqs = broadcast_pyobj(
work_reqs,
self.attn_cp_group.rank,
self.attn_cp_cpu_group,
src=self.attn_cp_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, abort_reqs = self.mm_receiver.process_waiting_requests(recv_reqs)
for req, error_msg, error_code in abort_reqs:
status_code = (
HTTPStatus.BAD_REQUEST
if error_code == 400
else HTTPStatus.INTERNAL_SERVER_ERROR
)
prepare_abort(req, error_msg, status_code=status_code)
self.stream_output([req], req.return_logprob)
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):
now = time.monotonic()
self.session_controller.maybe_reap(now)
for recv_req in recv_reqs:
# Skip health check when server is busy — ongoing requests already carry health info.
if is_health_check_generate_req(recv_req) and not self.is_fully_idle(
for_health_check=True
):
self.return_health_check_ipcs.append(
getattr(recv_req, "http_worker_ipc", None)
)
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.dp_tp_cpu_group is not None
):
group_world_size = torch.distributed.get_world_size(
group=self.dp_tp_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.dp_tp_group.rank_in_group == 0:
# 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.dp_tp_group.first_rank,
group=self.dp_tp_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.dp_tp_group.first_rank,
group=self.dp_tp_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 _maybe_clear_mm_inputs(self, batch: ScheduleBatch) -> None:
for req in batch.reqs:
if not req.finished() or not (mm_inputs := req.multimodal_inputs):
continue
# For session requests, keep mm_inputs for the next request
if req.session:
continue
# For non-session requests, clear features and mm_inputs
for item in mm_inputs.mm_items:
item.feature = None
req.multimodal_inputs = None
def handle_generate_request(
self,
recv_req: TokenizedGenerateReqInput,
):
# Route: normal request / session request / session-not-found
session_id = (
recv_req.session_params.id if recv_req.session_params is not None else None
)
if session_id is None:
# Normal non-session request
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,
routed_dp_rank=recv_req.routed_dp_rank,
disagg_prefill_dp_rank=recv_req.disagg_prefill_dp_rank,
vocab_size=self.model_config.vocab_size,
priority=recv_req.priority,
metrics_collector=(
self.metrics_collector if self.enable_metrics else None
),
routing_key=recv_req.routing_key,
http_worker_ipc=recv_req.http_worker_ipc,
dllm_config=self.dllm_config,
time_stats=recv_req.time_stats,
)
req.tokenizer = self.tokenizer
if self.disaggregation_mode != DisaggregationMode.NULL:
# Invalid request for disaggregated mode
if (
recv_req.bootstrap_room is None
and self.transfer_backend != TransferBackend.FAKE
):
error_msg = (
f"Invalid request: Disaggregated request received without "
f"bootstrap room id. {req.rid=}"
)
logger.error(error_msg)
recv_req.time_stats.trace_ctx.abort(
abort_info={"reason": error_msg}
)
prepare_abort(req, error_msg, status_code=HTTPStatus.BAD_REQUEST)
self.stream_output([req], req.return_logprob)
return
elif session_id in self.session_controller:
# Session exists: create request from session
session = self.session_controller.get(session_id)
req = session.create_req(
recv_req,
self.tokenizer,
self.model_config.vocab_size,
eos_token_ids=self.model_config.hf_eos_token_id,
)
# TODO: set trace context
if self.enable_metrics:
req.time_stats.set_metrics_collector(self.metrics_collector)
if isinstance(req.finished_reason, FINISH_ABORT):
self.init_req_max_new_tokens(req)
self._add_request_to_queue(req)
return
else:
# Session ID provided but session not found
req = Req(
recv_req.rid,
recv_req.input_text,
recv_req.input_ids,
recv_req.sampling_params,
vocab_size=self.model_config.vocab_size,
)
req.tokenizer = self.tokenizer
req.set_finish_with_abort(
f"Invalid request: session id {session_id} does not exist"
)
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)
SessionController.adjust_mm_offsets(recv_req, req, image_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
if not recv_req.return_logprob and recv_req.logprob_start_len != -1:
# When return_logprob is False, logprob_start_len should be ignored
recv_req.logprob_start_len = -1
if recv_req.logprob_start_len == -1:
if recv_req.return_logprob and recv_req.token_ids_logprob is None:
# If logprob is required but neither token_ids_logprob nor logprob_start_len is
# set, return the logprobs for output tokens by default
req.logprob_start_len = len(req.origin_input_ids)
elif req.is_prefill_only:
# For prefill-only requests with logprob_start_len == -1, set logprob_start_len
# beyond input sequence to skip input logprob computation entirely
req.logprob_start_len = len(req.origin_input_ids)
else:
# If return_logprob is False, only the last token requires logprob computation
req.logprob_start_len = -1
else:
req.logprob_start_len = recv_req.logprob_start_len
if 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 = -1
req.set_finish_with_abort(error_msg)
self._add_request_to_queue(req)
return
added_to_grammar_queue = self.grammar_manager.process_req_with_grammar(req)
if not added_to_grammar_queue:
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, cow_mamba=False)
last_host_node = (
req.last_host_backup_node
if req.last_host_backup_node is not None
else req.last_host_node
)
if last_host_node.backuped or last_host_node is self.tree_cache.root_node:
last_hash = 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 = (
last_host_node.get_prefix_hash_values(last_host_node.parent)
if self.tree_cache.hicache_storage_pass_prefix_keys
else None
)
self.tree_cache.prefetch_from_storage(
req.rid,
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.set_wait_queue_entry_time()
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.set_prefill_bootstrap_queue_entry_time()
elif self.disaggregation_mode == DisaggregationMode.DECODE:
self.disagg_decode_prealloc_queue.add(req, is_retracted=is_retracted)
if not is_retracted:
req.time_stats.set_decode_prealloc_queue_entry_time()
else:
req.time_stats.set_retract_time()
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,
)
req.time_stats.trace_ctx.abort(abort_info=abort_req.finished_reason)
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:
if self.enable_hicache_storage:
# Release prefetch events associated with the request
self.tree_cache.release_aborted_request(candidate_req.rid)
elif self.enable_hierarchical_cache:
self.tree_cache.terminate_prefetch(candidate_req.rid)
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,
)
req_to_abort.time_stats.trace_ctx.abort(abort_info={"reason": message})
return req_to_abort.rid == recv_req.rid
def _abort_on_waiting_timeout(self):
if (timeout_s := envs.SGLANG_REQ_WAITING_TIMEOUT.get()) <= 0:
return
deleted_reqs = set()
deadline = time.perf_counter() - timeout_s
for req in self.waiting_queue:
entry_time = req.time_stats.wait_queue_entry_time
if 0 < entry_time < deadline:
if self.enable_hicache_storage:
# Release prefetch events associated with the request
self.tree_cache.release_aborted_request(req.rid)
self.send_to_tokenizer.send_output(
AbortReq(
finished_reason={
"type": "abort",
"status_code": HTTPStatus.SERVICE_UNAVAILABLE,
"message": "Request waiting timeout reached.",
},
rid=req.rid,
),
req,
)
deleted_reqs.add(req)
if deleted_reqs:
self.waiting_queue = [
req for req in self.waiting_queue if req not in deleted_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,
token_type_ids=recv_req.token_type_ids,
routed_dp_rank=recv_req.routed_dp_rank,
priority=recv_req.priority,
dimensions=recv_req.dimensions,
lora_id=recv_req.lora_id,
http_worker_ipc=recv_req.http_worker_ipc,
time_stats=recv_req.time_stats,
)
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
# The `pad_input_ids_func` is model-specific and may be None for
# embedding models or models not requiring special padding.
# If None, `req.origin_input_ids` is expected to be correctly populated already.
if self.pad_input_ids_func:
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 = -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 stash_chunked_request(self, req: Req):
self.tree_cache.cache_unfinished_req(req, chunked=True)
def _build_hisparse_decode_batch(self, reqs):
"""Build a ScheduleBatch for hisparse requests transitioning from staging to decode."""
device = self.device
batch = ScheduleBatch.init_new(
reqs=reqs,
req_to_token_pool=self.req_to_token_pool,
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
tree_cache=self.tree_cache,
model_config=self.model_config,
enable_overlap=self.enable_overlap,
spec_algorithm=self.spec_algorithm,
)
batch.req_pool_indices = torch.tensor(
[r.req_pool_idx for r in reqs], dtype=torch.int64, device=device
)
seq_lens = [len(r.origin_input_ids) + len(r.output_ids) - 1 for r in reqs]
batch.seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=device)
batch.seq_lens_cpu = torch.tensor(seq_lens, dtype=torch.int64)
batch.orig_seq_lens = torch.tensor(seq_lens, dtype=torch.int32, device=device)
batch.seq_lens_sum = sum(seq_lens)
# output_ids = last generated token, used as input_ids by prepare_for_decode
batch.output_ids = torch.tensor(
[r.output_ids[-1] for r in reqs], dtype=torch.int64, device=device
)
# Set logprob fields if any request needs them
if batch.return_logprob:
batch.top_logprobs_nums = [r.top_logprobs_num for r in reqs]
batch.token_ids_logprobs = [list(r.origin_input_ids) for r in reqs]
# Build sampling info from scratch for these requests
batch.sampling_info = SamplingBatchInfo.from_schedule_batch(
batch, self.model_config.vocab_size
)
# todo hisparse, maybe other info to contain for the new batch
return batch
def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
self._abort_on_waiting_timeout()
self._abort_on_running_timeout()
if self.dllm_config is not None:
self.dllm_manager.filter_finished_reqs()
# Merge the prefill batch into the running batch
chunked_req_to_exclude = set()
if self.dllm_config is not None and self.dllm_manager.any_staging_reqs():
chunked_req_to_exclude.update(self.dllm_manager.staging_queue)
for req in self.dllm_manager.staging_queue:
self.stash_chunked_request(req)
if self.chunked_req is not None:
# 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.stash_chunked_request(self.chunked_req)
if self.enable_hisparse:
ready_reqs = self.hisparse_coordinator.collect_ready_reqs()
if len(ready_reqs) > 0:
new_batch = self._build_hisparse_decode_batch(ready_reqs)
if self.running_batch.is_empty():
self.running_batch = new_batch
else:
self.running_batch.merge_batch(new_batch)
self.running_batch.hisparse_coordinator = self.hisparse_coordinator
else:
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)
if self.dllm_config is not None and self.last_batch.reqs:
chunked_req_to_exclude.update(self.last_batch.reqs)
# 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.
if not self.last_batch.is_empty():
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)
# For prefill-only batch, filter out finished requests since they
# won't go through the decode step. This keeps running_batch accurate
# for load reporting (num_running_reqs via /get_load).
# Runs outside the last_batch block so stale requests are cleaned
# even when no new batches arrive (e.g. traffic stops).
if self.running_batch.is_prefill_only:
self.running_batch.filter_batch()
if self.dllm_config is not None:
new_batch = self.get_new_batch_dllm()
else:
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.maybe_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 (skip for prefill-only batches)
if (
not self.running_batch.is_empty()
and not self.running_batch.is_prefill_only
):
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 and log stats
ret = self.maybe_prepare_mlp_sync_batch(ret, need_sync=need_mlp_sync)
# Handle ngram embedding
ret = self._maybe_prepare_ngram_embedding(ret)
if ret:
set_schedule_time_batch(ret)
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]:
prefill_delayer_single_pass = None
if self.prefill_delayer:
# Get token usage from several pools
token_usage = None
if self.is_hybrid_swa:
_, _, full_token_usage, swa_token_usage, *_ = self._get_swa_token_info()
token_usage = max(full_token_usage, swa_token_usage)
if self.is_hybrid_ssm:
_, _, full_token_usage, mamba_token_usage, *_ = (
self._get_mamba_token_info()
)
token_usage = (
max(token_usage, mamba_token_usage)
if token_usage is not None
else max(full_token_usage, mamba_token_usage)
)
if token_usage is None:
_, token_usage, _, _ = self._get_token_info()
assert token_usage is not None
prefill_delayer_single_pass = PrefillDelayerSinglePassExecutor(
self.prefill_delayer, token_usage=token_usage
)
ret = self._get_new_batch_prefill_raw(
prefill_delayer_single_pass=prefill_delayer_single_pass
)
if self.prefill_delayer:
prefill_delayer_single_pass.finalize(actual_prefill=ret is not None)
return ret
def _get_new_batch_prefill_raw(
self, prefill_delayer_single_pass: Optional[PrefillDelayerSinglePassExecutor]
) -> Optional[ScheduleBatch]:
# Check if the grammar is ready in the grammar queue
if self.grammar_manager.has_waiting_grammars():
ready_grammar_requests = self.grammar_manager.get_ready_grammar_requests()
for req in ready_grammar_requests:
self._add_request_to_queue(req)
if self.enable_hierarchical_cache:
self.tree_cache.check_hicache_events()
if self.enable_priority_preemption:
# Reset batch_is_full to try preemption with a prefill adder.
self.running_batch.batch_is_full = False
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 requests has just been released.
# In PP case, chunked requests (or dllm requests) 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 requests to be added, otherwise, there will be a memory leak.
if (
self.get_num_allocatable_reqs(running_bs) <= 0
and self.chunked_req is not None
and not self.enable_priority_preemption
):
self.running_batch.batch_is_full = True
return None
# Get priority queue
self.policy.calc_priority(self.waiting_queue, self.running_batch)
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,
max_prefill_bs=self.max_prefill_bs,
max_running_requests=self.max_running_requests,
prefill_max_requests=self.server_args.prefill_max_requests,
prefill_delayer_single_pass=prefill_delayer_single_pass,
dllm_config=self.dllm_config,
)
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:
running_loras = {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 and req.lora_id not in running_loras:
if self.enable_lora_overlap_loading:
# For overlapping loading of LoRA weights with computation, we will load each adapter one at a time,
# as opposed to loading them in one batch
res = self.lora_overlap_loader.try_overlap_load_lora(
req.lora_id, running_loras
)
if not res:
continue
else:
new_lora_set = {req.lora_id} | running_loras
if not self.tp_worker.model_runner.lora_manager.validate_lora_batch(
new_lora_set
):
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.enable_priority_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
# Pop the number of tokens loaded from storage (L3 hits)
req.storage_hit_length = self.tree_cache.pop_prefetch_loaded_tokens(
req.rid
)
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 self.enable_lora:
running_loras.add(req.lora_id)
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
waiting_queue_before_prepare = list(self.waiting_queue)
chunked_req_before_prepare = self.chunked_req
can_run_set = set(can_run_list)
self.waiting_queue = [x for x in self.waiting_queue if x not in can_run_set]
if adder.preempt_list:
for req in adder.preempt_list:
self._add_request_to_queue(req)
if adder.new_chunked_req is not None:
# Update chunked prefill
assert self.chunked_req is None
self.chunked_req = adder.new_chunked_req
if self.chunked_req is not None:
self.chunked_req.is_chunked += 1
# Record for logging prefill stats after forward
self.adder = adder
self.can_run_list = can_run_list
self.running_bs = len(self.running_batch.reqs)
set_time_batch(can_run_list, "set_forward_entry_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,
)
self.max_prefill_bs = max(self.max_prefill_bs, len(can_run_list))
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()
)
try:
new_batch.prepare_for_extend()
except KVCapacityWaitError as exc:
self._release_prefill_adder_locks(
can_run_list, skip_req=chunked_req_before_prepare
)
self.waiting_queue = waiting_queue_before_prepare
self.chunked_req = chunked_req_before_prepare
self.running_batch.batch_is_full = True
logger.warning(
"[CP_SHARED_KV_CAPACITY_WAIT] prefill allocation deferred: %s",
exc,
)
return None
# Record prefill stats for logging after forward
new_batch.prefill_stats = PrefillStats.from_adder(
adder, self.running_batch.reqs, self.enable_priority_scheduling
)
# 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)
# mix_with_running cats input_ids but not input_embeds — shapes would mismatch
and new_batch.input_embeds is None
):
# 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 _release_prefill_adder_locks(
self, reqs: List[Any], *, skip_req: Optional[Any] = None
) -> None:
"""Release lock refs acquired by PrefillAdder for a failed prefill batch.
`PrefillAdder.add_one_req` persists one `inc_lock_ref` per staged
request after its short-lived scheduling lock context exits. If KV
allocation later reports a recoverable capacity wait, the batch never
runs, so those persistent refs must be dropped before the requests are
returned to the waiting queue.
"""
if getattr(self.tree_cache, "disable", False):
return
use_swa_params = self.tree_cache.supports_swa() and self.tree_cache.is_tree_cache()
for req in reqs:
if req is skip_req:
continue
last_node = getattr(req, "last_node", None)
if last_node is None:
continue
if use_swa_params:
params = DecLockRefParams(
swa_uuid_for_lock=getattr(req, "swa_uuid_for_lock", None)
)
self.tree_cache.dec_lock_ref(last_node, params)
if hasattr(req, "swa_uuid_for_lock"):
req.swa_uuid_for_lock = None
else:
self.tree_cache.dec_lock_ref(last_node)
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
# Eagerly release lock_ref on completed write-through nodes so they
# become evictable, improving batch scheduling headroom.
if self.enable_hierarchical_cache:
self.tree_cache.flush_write_through_acks()
# Check if decode out of memory
if (kv_full_retract_flag := not batch.check_decode_mem()) or (
TEST_RETRACT and self.forward_ct % TEST_RETRACT_INTERVAL == 0
):
old_available_tokens = self.token_to_kv_pool_allocator.available_size()
old_ratio = self.new_token_ratio
retracted_reqs, new_token_ratio, reqs_to_abort = batch.retract_decode(
self.server_args
)
new_available_tokens = self.token_to_kv_pool_allocator.available_size()
new_token_gained = new_available_tokens - old_available_tokens
self.num_retracted_reqs = len(retracted_reqs)
if self.enable_metrics and len(retracted_reqs) > 0:
self.metrics_collector.increment_retracted_reqs(
num_retracted_reqs=len(retracted_reqs),
num_retracted_input_tokens=sum(
len(r.origin_input_ids) for r in retracted_reqs
),
num_retracted_output_tokens=sum(
len(r.output_ids) for r in retracted_reqs
),
)
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(
finished_reason=abort_reason.to_json(),
rid=req.rid,
),
req,
)
msg_prefix = (
"KV cache pool is full. Retract requests. "
if kv_full_retract_flag
else "Testing retraction. "
)
msg_details = f"#retracted_reqs: {len(retracted_reqs)}, #new_tokens_gained: {new_token_gained}"
if kv_full_retract_flag:
msg_details += (
f", #new_token_ratio: {old_ratio:.4f} -> {new_token_ratio:.4f}"
)
logger.warning(msg_prefix + msg_details)
for req in retracted_reqs:
self._add_request_to_queue(req, is_retracted=True)
if self.enable_hisparse:
self.hisparse_coordinator.retract_req(req)
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
if batch.is_empty():
return batch
# 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 _prepare_hicache_write_backups_before_forward(
self, batch: ScheduleBatch
) -> None:
"""Reserve/register CP HiCache write-through backups immediately before forward.
CP shared-KV HiCache per-layer D2H backup must be registered after
`prepare_for_extend()` has allocated output KV slots, but before the
model starts emitting per-layer KV. Keeping this at the final
`run_batch` boundary prevents alternate prefill event loops from
bypassing the early-backup path and falling back to post-forward
catch-up copies.
"""
if not self.enable_hierarchical_cache:
return
if batch.forward_mode not in (
ForwardMode.EXTEND,
ForwardMode.SPLIT_PREFILL,
ForwardMode.DLLM_EXTEND,
):
return
prepare_fn = getattr(self.tree_cache, "prepare_write_backup_for_req", None)
if prepare_fn is None:
inner_cache = getattr(self.tree_cache, "inner", None)
prepare_fn = getattr(inner_cache, "prepare_write_backup_for_req", None)
if prepare_fn is None:
return
for req in batch.reqs:
prepare_fn(req)
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:
set_time_batch(batch.reqs, "set_prefill_run_batch_start_time")
# Place holder handling for pd-disagg decode event loop
if batch.forward_mode.is_prebuilt():
return self._run_batch_prebuilt(batch)
self._prepare_hicache_write_backups_before_forward(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.record_bubble_metrics(batch):
self.forward_stream.wait_stream(self.schedule_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_spec_v2:
# FIXME(lsyin): tmp code for spec 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:
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()
if self.enable_overlap:
self.record_batch_in_overlap(model_worker_batch)
with self.forward_stream_ctx, self.record_bubble_metrics(batch):
self.forward_stream.wait_stream(self.schedule_stream)
embeddings = self.tp_worker.forward_batch_embedding(
model_worker_batch
)
ret = EmbeddingBatchResult(embeddings=embeddings)
ret.copy_to_cpu()
else:
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:
set_time_batch(batch.reqs, "set_prefill_run_batch_end_time")
if (
self.server_args.enable_dp_attention
and self.server_args.elastic_ep_backend is not None
):
# Get the tensors indicating rank activeness
tp_active_ranks = self.tp_group.active_ranks.detach().cpu().numpy()
tp_active_ranks_cpu = self.tp_group.active_ranks_cpu.detach().numpy()
tp_active_ranks &= tp_active_ranks_cpu
dp_active_ranks = tp_active_ranks.reshape(self.dp_size, -1).prod(axis=1)
self.send_to_tokenizer.send_output(
ActiveRanksOutput(status=dp_active_ranks.tolist())
)
return ret
def launch_batch_sample_if_needed(
self, batch_result: GenerationBatchResult
) -> Union[GenerationBatchResult]:
# 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.schedule_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)
# Release the closure and large GPU tensors that are no longer needed.
# The delay_sample_func closure captures forward_batch (which holds
# sampling_info with vocab_mask) and logits_output (which holds
# next_token_logits). Without clearing these, they stay alive via
# batch_result in result_queue and batch_record_buf until the next
# iteration, causing a steady VRAM leak with structured output.
batch_result.delay_sample_func = None
if batch_result.logits_output is not None:
batch_result.logits_output.next_token_logits = None
def process_batch_result(
self,
batch: ScheduleBatch,
result: Union[GenerationBatchResult, EmbeddingBatchResult],
):
if batch.forward_mode.is_decode():
self.process_batch_result_decode(batch, result)
elif batch.forward_mode.is_extend():
if batch.is_dllm():
self.process_batch_result_dllm(batch, result)
elif self.disaggregation_mode == DisaggregationMode.PREFILL:
self.process_batch_result_disagg_prefill(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():
self.process_batch_result_idle(batch, result)
self.log_batch_result_stats(batch, result)
self._maybe_clear_mm_inputs(batch)
self.maybe_send_health_check_signal()
def maybe_send_health_check_signal(self):
if self.return_health_check_ipcs:
# 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.send_to_tokenizer.send_output(
HealthCheckOutput(
http_worker_ipc=self.return_health_check_ipcs.popleft()
)
)
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_fully_idle(self, for_health_check=False) -> bool:
# Health check piggybacks on running requests in process_output.
# Only running_batch + waiting_queue guarantee active GPU processing;
# disagg queues (bootstrap/prealloc/transfer) may have items without
# any request actually running on GPU — e.g. stuck handshake, full
# KV cache, or stalled transfer — so they can't carry health info.
# Batch running status
idle = (
self.running_batch.is_empty()
and self.chunked_req is None
and not self.dllm_manager.any_staging_reqs()
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))
)
# Waiting queues: waiting + bootstrapping + preallocation + kv transfer (decode)
idle &= len(self.waiting_queue) == 0
if not for_health_check:
# Grammar queue and prefill inflight queue may not produce batch
# results instantly, but they still indicate the server is not idle.
idle &= len(self.grammar_manager.grammar_queue) == 0
if self.disaggregation_mode == DisaggregationMode.PREFILL:
idle &= len(self.disagg_prefill_inflight_queue) == 0
idle &= len(self.disagg_prefill_bootstrap_queue.queue) == 0
if self.disaggregation_mode == DisaggregationMode.DECODE:
idle &= len(self.disagg_decode_prealloc_queue.queue) == 0
idle &= len(self.disagg_decode_transfer_queue.queue) == 0
# HiCache: in-flight async ops (GPU↔Host↔L3) must drain before
# destructive operations like attach/detach/flush_cache.
if self.enable_hierarchical_cache:
tc = self.tree_cache
idle &= len(tc.ongoing_write_through) == 0
idle &= len(tc.ongoing_load_back) == 0
if tc.enable_storage:
idle &= len(tc.ongoing_prefetch) == 0
idle &= len(tc.ongoing_backup) == 0
return idle
def attach_hicache_storage_wrapped(
self, recv_req: AttachHiCacheStorageReqInput
) -> AttachHiCacheStorageReqOutput:
if not self.enable_hierarchical_cache:
return AttachHiCacheStorageReqOutput(
success=False, message="Hierarchical cache is not enabled."
)
if not self.is_fully_idle():
return AttachHiCacheStorageReqOutput(
success=False,
message=(
"Reject attach: scheduler is not idle. "
f"#queue-req={len(self.waiting_queue)} "
f"#running-req={len(self.running_batch.reqs)}"
),
)
if not hasattr(self.tree_cache, "attach_storage_backend"):
return AttachHiCacheStorageReqOutput(
success=False,
message="Current tree_cache implementation does not support dynamic attach.",
)
try:
ok, msg = self.tree_cache.attach_storage_backend(
storage_backend=recv_req.hicache_storage_backend,
storage_backend_extra_config_json=recv_req.hicache_storage_backend_extra_config_json,
served_model_name=self.server_args.served_model_name,
hicache_storage_prefetch_policy=recv_req.hicache_storage_prefetch_policy,
hicache_write_policy=recv_req.hicache_write_policy,
)
except Exception as e:
logger.exception("Attach HiCache storage backend failed with exception.")
return AttachHiCacheStorageReqOutput(success=False, message=str(e))
if ok:
self.enable_hicache_storage = True
self.server_args.hicache_storage_backend = recv_req.hicache_storage_backend
if recv_req.hicache_storage_backend_extra_config_json is not None:
self.server_args.hicache_storage_backend_extra_config = (
recv_req.hicache_storage_backend_extra_config_json
)
if recv_req.hicache_storage_prefetch_policy is not None:
self.server_args.hicache_storage_prefetch_policy = (
recv_req.hicache_storage_prefetch_policy
)
if recv_req.hicache_write_policy is not None:
self.server_args.hicache_write_policy = recv_req.hicache_write_policy
logger.info(
f"Attached HiCache storage backend: {recv_req.hicache_storage_backend}"
)
return AttachHiCacheStorageReqOutput(success=ok, message=msg)
def detach_hicache_storage_wrapped(
self, recv_req: DetachHiCacheStorageReqInput
) -> DetachHiCacheStorageReqOutput:
if not self.enable_hierarchical_cache:
return DetachHiCacheStorageReqOutput(
success=False, message="Hierarchical cache is not enabled."
)
if not self.is_fully_idle():
return DetachHiCacheStorageReqOutput(
success=False,
message=(
"Reject detach: scheduler is not idle. "
f"#queue-req={len(self.waiting_queue)} "
f"#running-req={len(self.running_batch.reqs)}"
),
)
if not hasattr(self.tree_cache, "detach_storage_backend"):
return DetachHiCacheStorageReqOutput(
success=False,
message="Current tree_cache implementation does not support dynamic detach.",
)
# Idempotent detach: even if scheduler thinks storage is disabled, we still
# attempt best-effort cleanup in tree_cache (it may have leftover state).
try:
ok, msg = self.tree_cache.detach_storage_backend()
except Exception as e:
logger.exception("Detach HiCache storage backend failed with exception.")
return DetachHiCacheStorageReqOutput(success=False, message=str(e))
if ok or (not self.enable_hicache_storage):
# Treat "already disabled / nothing to do" as success for idempotence.
self.enable_hicache_storage = False
self.server_args.hicache_storage_backend = None
self.server_args.hicache_storage_backend_extra_config = None
logger.info("Detached HiCache storage backend.")
return DetachHiCacheStorageReqOutput(
success=True, message=msg or "HiCache storage backend is detached."
)
return DetachHiCacheStorageReqOutput(success=False, message=msg)
def pin_prefix_wrapped(self, recv_req: PinPrefixReqInput):
if not hasattr(self.tree_cache, "pin_prefix"):
return PinPrefixReqOutput(
success=False,
nodes_pinned=0,
message="PIN requires --enable-hierarchical-cache",
)
if getattr(self.tree_cache, "_max_pinned_tokens", 0) <= 0:
return PinPrefixReqOutput(
success=False,
nodes_pinned=0,
message="Pinning is disabled (SGLANG_HICACHE_MAX_PINNED_RATIO is 0)",
)
nodes_pinned, reject_reason = self.tree_cache.pin_prefix(
recv_req.token_ids, recv_req.ttl_seconds
)
if nodes_pinned == 0:
return PinPrefixReqOutput(
success=False,
nodes_pinned=0,
message=reject_reason or "No matching prefix found in cache to pin",
)
msg = f"Pinned {nodes_pinned} nodes (ttl={recv_req.ttl_seconds}s)"
if reject_reason:
msg += f"; {reject_reason}"
return PinPrefixReqOutput(
success=True,
nodes_pinned=nodes_pinned,
message=msg,
)
def flush_cache(self):
"""Flush the memory pool and cache."""
if self.is_fully_idle():
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()
self.grammar_manager.clear()
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),
}
ret["effective_max_running_requests_per_dp"] = self.max_running_requests
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):
# todo hisparse, release resources for abort requests in hisparse coordinator
# 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:
if self.enable_hisparse:
self.hisparse_coordinator.request_finished(req)
release_kv_cache(req, self.tree_cache)
release_req_to_metadata_buffer(
req, self.req_to_metadata_buffer_idx_allocator
)
# For disaggregation prefill mode, free the metadata buffer index
if self.disaggregation_mode == DisaggregationMode.PREFILL:
release_req_to_metadata_buffer(
req, self.req_to_metadata_buffer_idx_allocator
)
# For mamba radix cache
if (
req.mamba_pool_idx is not None
and self.disaggregation_mode != DisaggregationMode.DECODE
):
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
# 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.
self.grammar_manager.abort_requests(recv_req)
# 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()
# Abort requests already retracted to CPU cache
if self.disagg_decode_prealloc_queue.retracted_queue:
remaining_retracted = []
for decode_req in self.disagg_decode_prealloc_queue.retracted_queue:
if recv_req.abort_all or decode_req.rid.startswith(recv_req.rid):
assert hasattr(decode_req, "kv_cache_cpu")
del decode_req.kv_cache_cpu
self.send_to_tokenizer.send_output(
AbortReq(rid=decode_req.rid), decode_req
)
else:
remaining_retracted.append(decode_req)
self.disagg_decode_prealloc_queue.retracted_queue = remaining_retracted
# 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 load_lora_adapter_from_tensors(
self, recv_req: LoadLoRAAdapterFromTensorsReqInput
) -> LoadLoRAAdapterFromTensorsReqOutput:
"""In-place loading a new lora adapter from serialized tensors."""
result = self.tp_worker.load_lora_adapter_from_tensors(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):
return self.session_controller.open(recv_req)
def close_session(self, recv_req: CloseSessionReqInput):
self.session_controller.close(recv_req)
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
def handle_dumper_control(self, recv_req: DumperControlReqInput):
from sglang.srt.debug_utils.dumper import dumper
try:
response: list = []
if (
not torch.distributed.is_initialized()
or torch.distributed.get_rank() == 0
):
response = dumper._http_manager.handle_request(
method=recv_req.method, body=recv_req.body
)
self.send_to_tokenizer.send_output(
DumperControlReqOutput(success=True, response=response), recv_req
)
except Exception as e:
print(f"[Scheduler] handle_dumper_control error: {e}", flush=True)
self.send_to_tokenizer.send_output(
DumperControlReqOutput(success=False, response=[], error=str(e)),
recv_req,
)
# 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 = real_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 real_time() - self.last_empty_time > self.empty_cache_interval
):
self.last_empty_time = real_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 dispatch_event_loop(scheduler: Scheduler):
# Dispatch to the appropriate event loop based on the disaggregation mode
server_args = scheduler.server_args
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()
def configure_scheduler(
server_args: ServerArgs,
tp_rank: int,
attn_cp_rank: int,
moe_dp_rank: int,
moe_ep_rank: int,
pp_rank: int,
dp_rank: Optional[int],
) -> Optional[int]:
"""Configure scheduler worker: logging, process title, etc.
Returns:
dp_rank
"""
# Generate the logger 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"])
prefix = ""
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.attn_cp_size > 1:
prefix += f" ATTN_CP{attn_cp_rank}"
if server_args.moe_dp_size > 1:
prefix += f" MOE_DP{moe_dp_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()
# Configure the logger
configure_logger(server_args, prefix=prefix)
suppress_other_loggers()
return dp_rank
def run_scheduler_process(
server_args: ServerArgs,
port_args: PortArgs,
gpu_id: int,
tp_rank: int,
attn_cp_rank: int,
moe_dp_rank: int,
moe_ep_rank: int,
pp_rank: int,
dp_rank: Optional[int],
pipe_writer,
):
dp_rank = configure_scheduler(
server_args, tp_rank, attn_cp_rank, moe_dp_rank, moe_ep_rank, pp_rank, dp_rank
)
kill_itself_when_parent_died()
parent_process = psutil.Process().parent()
# 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
)
numa_node = None
if (numa_nodes := server_args.numa_node) is not None:
numa_node = numa_nodes[gpu_id]
elif envs.SGLANG_AUTO_NUMA_BIND.get():
numa_node = get_numa_node(gpu_id)
logger.info(f"auto get NUMA node {numa_node} for GPU {gpu_id}")
if numa_node is not None and not envs.SGLANG_NUMA_BIND_V2.get():
numa_bind_to_node(numa_node)
# 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
scheduler = None
try:
scheduler = Scheduler(
server_args,
port_args,
gpu_id,
tp_rank,
moe_ep_rank,
pp_rank,
attn_cp_rank,
moe_dp_rank,
dp_rank,
)
# Send initialization info back to the parent process
pipe_writer.send(scheduler.get_init_info())
# Run the event loop (blocks until shutdown)
scheduler.run_event_loop()
except Exception as e:
traceback = get_exception_traceback()
if scheduler is not None and scheduler.is_unrecoverable_error(e):
logger.critical(
f"Scheduler hit an unrecoverable error: {traceback}"
)
scheduler.handle_graceful_shutdown(e)
else:
logger.error(f"Scheduler hit an exception: {traceback}")
parent_process.send_signal(signal.SIGQUIT)
Reference in New Issue View Git Blame Copy Permalink