config: read resolved config via namespace accessors (#31814)

python/sglang/srt/batch_overlap/two_batch_overlap.py

@@ -39,7 +39,12 @@ from sglang.srt.model_executor.forward_batch_info import (
     compute_position,
 )
 from sglang.srt.model_executor.forward_context import get_attn_backend
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_device,
+    get_exec,
+    get_parallel,
+    get_server_args,
+)
 from sglang.srt.speculative.spec_info import SpecInput
 from sglang.srt.utils import BumpAllocator, empty_context, get_bool_env_var, is_hip
 
@@ -183,7 +188,7 @@ def _update_device_and_sum_field_from_cpu_field(
         cpu_value
         if isinstance(cpu_value, torch.Tensor)
         else torch.tensor(cpu_value, dtype=old_device_value.dtype)
-    ).to(device=get_server_args().device, non_blocking=True)
+    ).to(device=get_device().device, non_blocking=True)
     setattr(batch, device_field, new_device_value)
 
     if sum_field is not None:
@@ -335,7 +340,7 @@ def compute_split_indices_for_cuda_graph_replay(
 class TboCudaGraphRunnerPlugin:
     def __init__(self):
         self._tbo_children_num_token_non_padded = torch.zeros(
-            (2,), dtype=torch.int32, device=get_server_args().device
+            (2,), dtype=torch.int32, device=get_device().device
         )
 
     def capture_one_batch_size(self, batch: ForwardBatch, num_tokens: int):
@@ -633,7 +638,7 @@ class TboForwardBatchPreparer:
             sum_field=None,
         )
         _, child_b.extend_start_loc = compute_position(
-            get_server_args().attention_backend,
+            get_exec().kernel.attention_backend,
             child_b.extend_prefix_lens,
             child_b.extend_seq_lens,
             child_b.extend_num_tokens,
@@ -832,7 +837,7 @@ class TboForwardBatchPreparer:
         value_a = min(tbo_split_token_index, num_token_non_padded)
         value_b = max(0, num_token_non_padded - tbo_split_token_index)
         return torch.tensor([value_a, value_b], dtype=torch.int32).to(
-            device=get_server_args().device, non_blocking=True
+            device=get_device().device, non_blocking=True
         )
 
     @classmethod

python/sglang/srt/configs/inkling.py

@@ -8,6 +8,7 @@ from transformers import CONFIG_MAPPING
 from transformers.configuration_utils import PretrainedConfig
 
 from sglang.srt.configs.mamba_utils import BaseLinearStateParams
+from sglang.srt.runtime_context import get_exec
 
 
 class InklingModelConfig(PretrainedConfig):
@@ -224,9 +225,8 @@ class InklingModelConfig(PretrainedConfig):
             self.swa_num_key_value_heads, self.swa_head_dim
         )
         stream_dim = self.hidden_size
-        from sglang.srt.runtime_context import get_server_args
 
-        if get_server_args().enable_scattered_sconv:
+        if get_exec().comm.enable_scattered_sconv:
             # Scattered sconv: the attn/mlp output sconvs run on the [T, H/P]
             # hidden shard, so their conv-state caches shard with them.
             assert (

python/sglang/srt/constrained/grammar_manager.py

@@ -14,6 +14,7 @@ from sglang.srt.constrained.base_grammar_backend import (
 from sglang.srt.constrained.reasoner_grammar_backend import ReasonerGrammarObject
 from sglang.srt.distributed.communication_tags import P2PTag
 from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_serving
 
 if TYPE_CHECKING:
     from sglang.srt.managers.io_struct import AbortReq
@@ -28,7 +29,7 @@ class GrammarManager:
         self.scheduler = scheduler
         self.server_args = scheduler.server_args
         self.grammar_queue: List[Req] = []
-        if not self.server_args.skip_tokenizer_init:
+        if not get_serving().skip_tokenizer_init:
             self.grammar_backend = create_grammar_backend(
                 self.server_args,
                 scheduler.tokenizer,

python/sglang/srt/disaggregation/common/conn.py

@@ -32,11 +32,8 @@ from sglang.srt.disaggregation.utils import (
 )
 from sglang.srt.distributed import get_pp_group, get_world_group
 from sglang.srt.environ import envs
-from sglang.srt.layers.dp_attention import (
-    get_attention_dp_rank,
-    get_attention_dp_size,
-)
-from sglang.srt.runtime_context import get_model, get_parallel
+from sglang.srt.layers.dp_attention import get_attention_dp_rank, get_attention_dp_size
+from sglang.srt.runtime_context import get_model, get_parallel, get_serving
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.network import (
     NetworkAddress,
@@ -634,7 +631,7 @@ class CommonKVManager(BaseKVManager):
             # Self-register the HTTP API port so the decode can derive the PD
             # retract rebootstrap /generate URL from bootstrap info instead of a
             # router-injected pd_rebootstrap_prefill_url.
-            "prefill_http_port": self.server_args.port,
+            "prefill_http_port": get_serving().port,
         }
 
         max_retries, initial_delay, max_delay = 5, 1.0, 30.0

python/sglang/srt/disaggregation/decode.py

@@ -86,7 +86,7 @@ from sglang.srt.observability.req_time_stats import (
     set_schedule_time_batch,
     set_time_batch,
 )
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_disagg, get_parallel
 from sglang.srt.utils import get_num_new_pages
 from sglang.srt.utils.network import NetworkAddress
 from sglang.srt.utils.nvtx_utils import scheduler_nvtx_method
@@ -2151,7 +2151,7 @@ class SchedulerDisaggregationDecodeMixin:
                 # Decode-radix path: new requests already matched in
                 # `pop_preallocated`. Retracted requests reset `last_node`,
                 # so re-match only when that state is missing.
-                if self.server_args.disaggregation_decode_enable_radix_cache:
+                if get_disagg().disaggregation_decode_enable_radix_cache:
                     tree_cache = self.tree_cache if req.last_node is None else None
                 else:
                     tree_cache = self.tree_cache
@@ -2191,7 +2191,7 @@ class SchedulerDisaggregationDecodeMixin:
         if self.enable_decode_hicache:
             self.tree_cache.check_hicache_events()
 
-        if self.server_args.disaggregation_decode_enable_offload_kvcache:
+        if get_disagg().disaggregation_decode_enable_offload_kvcache:
             self.decode_offload_manager.check_offload_progress()
 
         # try to resume retracted requests if there are enough space for another `num_reserved_decode_tokens` decode steps
@@ -2203,9 +2203,7 @@ class SchedulerDisaggregationDecodeMixin:
 
         if not hasattr(self, "polling_count"):
             self.polling_count = 0
-            self.polling_interval = (
-                self.server_args.disaggregation_decode_polling_interval
-            )
+            self.polling_interval = get_disagg().disaggregation_decode_polling_interval
 
         self.polling_count = (self.polling_count + 1) % self.polling_interval
 

python/sglang/srt/disaggregation/encode_grpc_server.py

@@ -28,6 +28,7 @@ from sglang.srt.disaggregation.encode_server import (
 )
 from sglang.srt.managers.io_struct import async_sock_send, wrap_as_pickle
 from sglang.srt.managers.schedule_batch import Modality
+from sglang.srt.runtime_context import get_disagg
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import random_uuid
 from sglang.srt.utils.network import NetworkAddress, get_zmq_socket
@@ -117,13 +118,13 @@ class SGLangEncoderServer(SGLangEncoderServicer):
                 context.set_details(error_msg)
                 return sglang_encoder_pb2.EncodeResponse()
 
-            if self.server_args.encoder_transfer_backend == "mooncake":
+            if get_disagg().encoder_transfer_backend == "mooncake":
                 return sglang_encoder_pb2.EncodeResponse(
                     embedding_size=nbytes,
                     embedding_len=embedding_len,
                     embedding_dim=embedding_dim,
                 )
-            elif self.server_args.encoder_transfer_backend == "zmq_to_scheduler":
+            elif get_disagg().encoder_transfer_backend == "zmq_to_scheduler":
                 embedding_ports = list(request.embedding_port)
                 logger.info(f"embedding_port = {embedding_ports}")
                 if not embedding_ports:
@@ -141,7 +142,7 @@ class SGLangEncoderServer(SGLangEncoderServicer):
                     await asyncio.gather(*tasks)
                     self.encoder.embedding_to_send.pop(request.req_id, None)
                 return sglang_encoder_pb2.EncodeResponse()
-            elif self.server_args.encoder_transfer_backend == "zmq_to_tokenizer":
+            elif get_disagg().encoder_transfer_backend == "zmq_to_tokenizer":
                 embedding_port = (
                     request.embedding_port[0] if request.embedding_port else 0
                 )

python/sglang/srt/disaggregation/encode_server.py

@@ -59,14 +59,9 @@ from sglang.srt.model_loader import get_model
 from sglang.srt.multimodal.processors.qwen_vl import preprocess_video
 from sglang.srt.observability.metrics_collector import EncoderMetricsCollector
 from sglang.srt.observability.req_time_stats import EncoderReqTimeStats
-from sglang.srt.observability.trace import (
-    process_tracing_init,
-    trace_set_thread_info,
-)
-from sglang.srt.server_args import (
-    PortArgs,
-    ServerArgs,
-)
+from sglang.srt.observability.trace import process_tracing_init, trace_set_thread_info
+from sglang.srt.runtime_context import get_disagg, get_exec, get_mm
+from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import (
     add_prometheus_middleware,
     configure_logger,
@@ -350,7 +345,7 @@ class MMEncoder:
             [], dtype=self._embedding_dtype
         ).element_size()
 
-        if self.server_args.enable_mm_global_cache:
+        if get_mm().enable_mm_global_cache:
             from sglang.srt.mem_cache.storage.mooncake_store.embedding_cache_controller import (
                 EmbeddingCacheController,
             )
@@ -368,15 +363,15 @@ class MMEncoder:
             self.mm_global_cache = None
 
         # Pre-compute embedding metadata (needed by all ranks for mooncake)
-        if self.server_args.encoder_transfer_backend == "mooncake":
+        if get_disagg().encoder_transfer_backend == "mooncake":
             self._embedding_dims = self._infer_embedding_dims()
 
         if self.rank == 0:
             logger.info(
-                f"Using transfer backend: {self.server_args.encoder_transfer_backend}"
+                f"Using transfer backend: {get_disagg().encoder_transfer_backend}"
             )
 
-            if self.server_args.encoder_transfer_backend == "mooncake":
+            if get_disagg().encoder_transfer_backend == "mooncake":
                 self.local_ip = get_local_ip_auto()
 
                 self.engine = get_mooncake_transfer_engine()
@@ -389,8 +384,8 @@ class MMEncoder:
                         hostname=self.local_ip,
                         gpu_id=self.gpu_id,
                         ib_device=(
-                            self.server_args.disaggregation_ib_device
-                            or self.server_args.mooncake_ib_device
+                            get_disagg().disaggregation_ib_device
+                            or get_exec().moe.mooncake_ib_device
                         ),
                     )
 
@@ -399,7 +394,7 @@ class MMEncoder:
             self.encode_dispatch_lock = asyncio.Lock()
 
             # Async mooncake state: track background VIT forward completion
-            if self.server_args.encoder_transfer_backend == "mooncake":
+            if get_disagg().encoder_transfer_backend == "mooncake":
                 self._forward_ready_events: Dict[str, asyncio.Event] = {}
                 self._forward_results: Dict[str, dict] = {}
                 # when multiple decoder TP ranks call
@@ -413,12 +408,12 @@ class MMEncoder:
 
         # Bind unified encode entry point based on backend and cache config
         if self.mm_global_cache is not None:
-            if self.server_args.encoder_transfer_backend == "mooncake":
+            if get_disagg().encoder_transfer_backend == "mooncake":
                 self._encode_fn = self.encode_with_global_cache_mooncake
             else:
                 self._encode_fn = self.encode_with_global_cache
         else:
-            if self.server_args.encoder_transfer_backend == "mooncake":
+            if get_disagg().encoder_transfer_backend == "mooncake":
                 self._encode_fn = self.encode_with_mooncake
             else:
                 self._encode_fn = self.encode
@@ -1688,7 +1683,7 @@ class MMEncoder:
                 mm_item.set(k, _convert(v))
 
             cache_hit = False
-            use_mm_cache = self.server_args.enable_prefix_mm_cache and log_metrics
+            use_mm_cache = get_mm().enable_prefix_mm_cache and log_metrics
             if use_mm_cache:
                 mm_item.set_pad_value()
                 mm_hash = MultiModalStaticCache.combine_hashes([mm_item.hash])
@@ -1784,7 +1779,7 @@ class MMEncoder:
         embedding_port=None,
         url=None,
     ):
-        if self.server_args.encoder_transfer_backend == "mooncake":
+        if get_disagg().encoder_transfer_backend == "mooncake":
             # Wait for async VIT forward completion if needed
             req_id = mm_data.req_id
             if req_id in self._forward_ready_events:
@@ -1855,7 +1850,7 @@ class MMEncoder:
         logger.info(f"{endpoint = }")
 
         # Serialize data
-        if self.server_args.encoder_transfer_backend == "mooncake":
+        if get_disagg().encoder_transfer_backend == "mooncake":
             # Mooncake already pushed the embedding via RDMA;
             new_mm_data = mm_data.copy_without_embedding()
             serialized_data = pickle.dumps(new_mm_data)
@@ -1887,11 +1882,11 @@ class MMEncoder:
         await asyncio.get_event_loop().run_in_executor(self.executor, send_with_socket)
         if (
             encoder_metrics_collector is not None
-            and self.server_args.encoder_transfer_backend != "mooncake"
+            and get_disagg().encoder_transfer_backend != "mooncake"
         ):
             encoder_metrics_collector.observe_transfer(
                 time.perf_counter() - _zmq_xfer_start,
-                backend=self.server_args.encoder_transfer_backend,
+                backend=get_disagg().encoder_transfer_backend,
             )
 
     async def encode(

python/sglang/srt/disaggregation/mooncake/conn.py

@@ -55,6 +55,7 @@ from sglang.srt.observability.trace import (
     TraceReqContext,
     trace_set_thread_info,
 )
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.network import NetworkAddress
 
@@ -314,9 +315,7 @@ class MooncakeKVManager(CommonKVManager):
         self.kv_buffer_tensors = None
 
     def _handle_staging_req(self, msg):
-        from sglang.srt.disaggregation.common.staging_handler import (
-            handle_staging_req,
-        )
+        from sglang.srt.disaggregation.common.staging_handler import handle_staging_req
 
         room = int(msg[1].decode("ascii"))
         session_id = msg[4].decode("ascii")
@@ -350,9 +349,7 @@ class MooncakeKVManager(CommonKVManager):
     def _is_watermark_ready(
         self, session_id: str, alloc_round: int, alloc_end: int
     ) -> bool:
-        from sglang.srt.disaggregation.common.staging_handler import (
-            is_watermark_ready,
-        )
+        from sglang.srt.disaggregation.common.staging_handler import is_watermark_ready
 
         return is_watermark_ready(self._staging_ctx, session_id, alloc_round, alloc_end)
 
@@ -469,7 +466,7 @@ class MooncakeKVManager(CommonKVManager):
             room,
             self.transfer_infos,
             self.kv_buffer_tensors,
-            self.server_args.chunked_prefill_size,
+            get_schedule().chunked_prefill_size,
             self._staging_ctx.prefetch_requested,
             self._staging_ctx.prefetch_sockets,
         )

python/sglang/srt/disaggregation/nixl/conn.py

@@ -13,6 +13,8 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
 import numpy as np
 import numpy.typing as npt
 
+from sglang.srt.runtime_context import get_schedule
+
 if TYPE_CHECKING:
     from sglang.srt.disaggregation.common.staging_handler import StagingTransferInfo
 
@@ -535,9 +537,7 @@ class NixlKVManager(CommonKVManager):
     def _is_watermark_ready(
         self, agent_name: str, alloc_round: int, alloc_end: int
     ) -> bool:
-        from sglang.srt.disaggregation.common.staging_handler import (
-            is_watermark_ready,
-        )
+        from sglang.srt.disaggregation.common.staging_handler import is_watermark_ready
 
         return is_watermark_ready(self._staging_ctx, agent_name, alloc_round, alloc_end)
 
@@ -558,9 +558,7 @@ class NixlKVManager(CommonKVManager):
         threading.Thread(target=decode_staging_thread, daemon=True).start()
 
     def _handle_staging_req(self, msg):
-        from sglang.srt.disaggregation.common.staging_handler import (
-            handle_staging_req,
-        )
+        from sglang.srt.disaggregation.common.staging_handler import handle_staging_req
 
         room = int(msg[1].decode("ascii"))
         session_id = msg[4].decode("ascii")
@@ -625,7 +623,7 @@ class NixlKVManager(CommonKVManager):
             room,
             self.transfer_infos,
             self.kv_buffer_tensors,
-            self.server_args.chunked_prefill_size,
+            get_schedule().chunked_prefill_size,
             self._staging_ctx.prefetch_requested,
             self._staging_ctx.prefetch_sockets,
         )
@@ -1739,9 +1737,7 @@ class NixlKVManager(CommonKVManager):
             req, page_start, num_pages, session_id=req.agent_name
         )
         if not ready:
-            from sglang.srt.disaggregation.common.staging_buffer import (
-                StagingAllocator,
-            )
+            from sglang.srt.disaggregation.common.staging_buffer import StagingAllocator
 
             if c_offset == StagingAllocator.ALLOC_OVERSIZED:
                 raise RuntimeError(

python/sglang/srt/disaggregation/prefill.py

@@ -64,6 +64,7 @@ from sglang.srt.mem_cache.common import (
 )
 from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
 from sglang.srt.observability.req_time_stats import set_schedule_time_batch
+from sglang.srt.runtime_context import get_disagg
 from sglang.srt.utils.nvtx_utils import scheduler_nvtx_method
 
 if TYPE_CHECKING:
@@ -1181,7 +1182,7 @@ class SchedulerDisaggregationPrefillMixin:
 
     def optimistic_release_and_requeue(self: Scheduler, req: Req) -> None:
         """Release KV cache and requeue an optimistic prefill request."""
-        max_attempts = self.server_args.optimistic_prefill_attempts
+        max_attempts = get_disagg().optimistic_prefill_attempts
         maybe_cache_unfinished_req(req, self.tree_cache)
         release_kv_cache(req, self.tree_cache)
         req.reset_for_retract()

python/sglang/srt/distributed/device_communicators/pymscclpp.py

@@ -14,7 +14,7 @@ from sglang.srt.compilation.compile_phase import (
 from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
     is_in_tc_piecewise_cuda_graph,
 )
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 
 logger = logging.getLogger(__name__)
 
@@ -25,7 +25,7 @@ class PyMscclppCommunicator:
 
     def _is_symm_mem_enabled(self) -> bool:
         try:
-            return get_server_args().enable_symm_mem
+            return get_exec().comm.enable_symm_mem
         except ValueError:
             return False
 

python/sglang/srt/distributed/device_communicators/pynccl_allocator.py

@@ -15,7 +15,7 @@ from torch.cuda.memory import (
 
 from sglang.srt.distributed.parallel_state import GroupCoordinator
 from sglang.srt.environ import envs
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils.common import torch_release
 
 after_2_8_0 = torch_release >= (2, 8)
@@ -159,7 +159,7 @@ _register_func = None
 
 def is_symmetric_memory_enabled():
     try:
-        return get_server_args().enable_symm_mem
+        return get_exec().comm.enable_symm_mem
     except ValueError:
         return False
 

python/sglang/srt/distributed/device_communicators/torch_symm_mem.py

@@ -12,6 +12,7 @@ from sglang.srt.distributed.device_communicators.all_reduce_utils import (
     TORCH_SYMM_MEM_ALL_REDUCE_MAX_SIZES,
 )
 from sglang.srt.environ import envs
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import is_cuda, is_hip
 
 try:
@@ -98,10 +99,9 @@ class TorchSymmMemCommunicator:
         # ([16384, 6144] bf16 = 192 MiB), including room for tail regions.
         if envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get():
             self.max_size = max(self.max_size, 256 * 1024 * 1024)
-            from sglang.srt.runtime_context import get_server_args
 
             if (
-                get_server_args().enable_scattered_sconv
+                get_exec().comm.enable_scattered_sconv
                 or envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV.get()
             ):
                 # Fused extend kernels are out-of-place, so OUT must hold the

python/sglang/srt/dllm/mixin/scheduler.py

@@ -11,6 +11,7 @@ from sglang.srt.managers.schedule_policy import AddReqResult, PrefillAdder
 from sglang.srt.mem_cache.common import release_kv_cache
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.observability.req_time_stats import set_time_batch
+from sglang.srt.runtime_context import get_exec, get_schedule
 
 logger = logging.getLogger(__name__)
 
@@ -22,7 +23,7 @@ class SchedulerDllmMixin:
     def init_diffusion_llm(self: Scheduler):
         self.dllm_config = (
             DllmConfig.from_server_args(self.server_args)
-            if self.server_args.dllm_algorithm is not None
+            if get_exec().dllm.dllm_algorithm is not None
             else None
         )
         self.dllm_manager = DllmManager(dllm_config=self.dllm_config)
@@ -200,7 +201,7 @@ class SchedulerDllmMixin:
             self.chunked_prefill_size,
             running_bs if self.is_mixed_chunk else 0,
             self.priority_scheduling_preemption_threshold,
-            prefill_max_requests=self.server_args.prefill_max_requests,
+            prefill_max_requests=get_schedule().prefill_max_requests,
             dllm_config=self.dllm_config,
         )
 

python/sglang/srt/elastic_ep/elastic_ep.py

@@ -442,7 +442,7 @@ def get_healthy_expert_location_src_rank(
     *, invoked_in_elastic_ep_rejoin_path: bool
 ) -> int:
     world_group = get_world_group()
-    # NOTE: do not key off `self.server_args.elastic_ep_rejoin` here.
+    # NOTE: do not key off `get_exec().moe.elastic_ep_rejoin` here.
     # A rank that was started as a rejoin rank may later act as a healthy
     # rank in a subsequent recovery cycle.
     local_rejoin_flag = bool(invoked_in_elastic_ep_rejoin_path)

python/sglang/srt/elastic_ep/expert_backup_client.py

@@ -7,13 +7,11 @@ from typing import Any, Callable
 import torch
 import zmq
 
-from sglang.srt.distributed.parallel_state import (
-    get_world_group,
-    get_world_size,
-)
+from sglang.srt.distributed.parallel_state import get_world_group, get_world_size
 from sglang.srt.environ import envs
 from sglang.srt.eplb.expert_location import get_global_expert_location_metadata
 from sglang.srt.managers.io_struct import UpdateExpertBackupReq, sock_recv, sock_send
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.network import get_local_ip_auto
 
@@ -111,7 +109,7 @@ class ExpertBackupClient:
         global_expert_location_metadata = get_global_expert_location_metadata()
         num_experts = (
             self.model_config.hf_config.n_routed_experts
-            + self.server_args.ep_num_redundant_experts
+            + get_exec().moe.ep_num_redundant_experts
         )
         num_local_experts = num_experts // self.moe_ep_size
         for i in range(self.engine_num):

python/sglang/srt/entrypoints/engine.py

@@ -877,7 +877,14 @@ class Engine(EngineScoreMixin, EngineBase):
                 server_args, port_args
             )
         else:
-            # Launch multi-tokenizer router
+            # Launch multi-tokenizer router. Unlike TokenizerManager, the router
+            # does not publish; but it runs in this parent process and reads
+            # resolved config through the namespace accessors (e.g. get_parallel()
+            # for routed_dp_rank), so publish here. The child TokenizerWorkers
+            # publish independently in their own processes.
+            from sglang.srt.runtime_context import publish
+
+            publish(server_args, role="tokenizer")
             tokenizer_manager = MultiTokenizerRouter(server_args, port_args)
             template_manager = None
 
@@ -997,12 +1004,18 @@ class Engine(EngineScoreMixin, EngineBase):
         )
 
     def get_server_info(self):
+        from sglang.srt.runtime_context import get_context
+
         internal_states = self.loop.run_until_complete(
             self.tokenizer_manager.get_internal_state()
         )
         return msgspec_to_builtins(
             {
-                **dataclasses.asdict(self.tokenizer_manager.server_args),
+                # Overlay post-publish overrides so the report reflects current
+                # config (weight version, model path, runtime tunables).
+                **get_context().resolved_server_args_dict(
+                    base=dataclasses.asdict(self.tokenizer_manager.server_args)
+                ),
                 **self._scheduler_init_result.scheduler_infos[0],
                 "internal_states": internal_states,
                 "version": __version__,

python/sglang/srt/entrypoints/grpc_bridge.py

@@ -15,6 +15,7 @@ from typing import Any, Awaitable, Callable, Dict, List, Optional
 
 from pydantic import ValidationError
 
+from sglang.srt.runtime_context import get_context, get_lora, get_serving
 from sglang.srt.utils.msgspec_utils import msgspec_to_builtins
 
 logger = logging.getLogger(__name__)
@@ -229,9 +230,7 @@ class RuntimeHandle:
             return self._openai_serving_classes
 
         from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat
-        from sglang.srt.entrypoints.openai.serving_classify import (
-            OpenAIServingClassify,
-        )
+        from sglang.srt.entrypoints.openai.serving_classify import OpenAIServingClassify
         from sglang.srt.entrypoints.openai.serving_completions import (
             OpenAIServingCompletion,
         )
@@ -376,16 +375,20 @@ class RuntimeHandle:
         model_config = self.tokenizer_manager.model_config
         result = {
             "model_path": self.tokenizer_manager.model_path,
-            "tokenizer_path": self.server_args.tokenizer_path,
+            "tokenizer_path": get_serving().tokenizer_path,
             "is_generation": self.tokenizer_manager.is_generation,
-            "weight_version": self.server_args.weight_version,
+            "weight_version": get_serving().weight_version,
             "model_type": getattr(model_config.hf_config, "model_type", None),
             "architectures": getattr(model_config.hf_config, "architectures", None),
         }
         return json.dumps(result, default=str)
 
     def get_server_info(self) -> str:
-        result: Dict[str, Any] = dataclasses.asdict(self.server_args)
+        # Overlay post-publish overrides (weight version, model path, runtime
+        # tunables) so the report reflects current config, not the startup record.
+        result: Dict[str, Any] = get_context().resolved_server_args_dict(
+            base=dataclasses.asdict(self.server_args)
+        )
         result.update(self.scheduler_info)
         return json.dumps(msgspec_to_builtins(result), default=str)
 
@@ -424,9 +427,7 @@ class RuntimeHandle:
                 "max_model_len": self.tokenizer_manager.model_config.context_len,
             }
         ]
-        if self.server_args.enable_lora and hasattr(
-            self.tokenizer_manager, "lora_registry"
-        ):
+        if get_lora().enable_lora and hasattr(self.tokenizer_manager, "lora_registry"):
             lora_registry = self.tokenizer_manager.lora_registry
             for _, lora_ref in lora_registry.get_all_adapters().items():
                 models.append(

python/sglang/srt/entrypoints/http_server.py

@@ -693,18 +693,19 @@ async def get_model_info():
 @app.get("/model_info")
 async def model_info():
     """Get the model information."""
+    from sglang.srt.runtime_context import get_serving
+
     model_config = _global_state.tokenizer_manager.model_config
     result = {
         "model_path": _global_state.tokenizer_manager.model_path,
         "tokenizer_path": _global_state.tokenizer_manager.server_args.tokenizer_path,
         "is_generation": _global_state.tokenizer_manager.is_generation,
         "preferred_sampling_params": _global_state.tokenizer_manager.server_args.preferred_sampling_params,
-        "weight_version": _global_state.tokenizer_manager.server_args.weight_version,
+        "weight_version": get_serving().weight_version,
         "has_image_understanding": model_config.is_image_understandable_model,
         "has_audio_understanding": model_config.is_audio_understandable_model,
         "model_type": getattr(model_config.hf_config, "model_type", None),
         "architectures": getattr(model_config.hf_config, "architectures", None),
-        "weight_version": _global_state.tokenizer_manager.server_args.weight_version,
         # "hf_config": model_config.hf_config.to_dict(),
     }
     return result
@@ -738,12 +739,18 @@ async def server_info():
         await _global_state.tokenizer_manager.get_internal_state()
     )
 
+    from sglang.srt.runtime_context import get_context
+
     server_args = _global_state.tokenizer_manager.server_args
 
     # server_args.model_config is not serializable but should be excluded by asdict.
+    # Overlay post-publish overrides so runtime updates (weight version, model
+    # path/load format) are reported, not the startup record.
     return msgspec_to_builtins(
         {
-            **dataclasses.asdict(server_args),
+            **get_context().resolved_server_args_dict(
+                base=dataclasses.asdict(server_args)
+            ),
             **_global_state.scheduler_info,
             "internal_states": internal_states,
             "version": __version__,
@@ -1368,7 +1375,9 @@ async def update_weight_version(
     # since weight_version update is a simple operation that doesn't affect model weights
     try:
         # Update the weight version in server args (the single source of truth)
-        _global_state.tokenizer_manager.server_args.override(
+        from sglang.srt.runtime_context import get_context
+
+        get_context().override(
             "http.update_weight_version", weight_version=obj.new_version
         )
 

python/sglang/srt/entrypoints/http_server_engine.py

@@ -55,6 +55,8 @@ class HttpServerEngineAdapter(EngineBase):
 
     def __init__(self, **kwargs):
         self.server_args = ServerArgs(**kwargs)
+        # Read host/port from the adapter's own args: no config is published yet
+        # in this process (publish happens in the child from launch_server_process).
         print(
             f"Launch HttpServerEngineAdapter at: {self.server_args.host}:{self.server_args.port}"
         )

python/sglang/srt/entrypoints/openai/realtime/session.py

@@ -72,6 +72,7 @@ from sglang.srt.entrypoints.openai.transcription_adapters.base import (
     TranscriptionAdapter,
 )
 from sglang.srt.managers.tokenizer_manager import TokenizerManager
+from sglang.srt.runtime_context import get_serving
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import random_uuid
 
@@ -338,12 +339,12 @@ class RealtimeConnection:
         if (
             transcription is not None
             and transcription.model
-            and transcription.model != self.server_args.served_model_name
+            and transcription.model != get_serving().served_model_name
         ):
             await self._send_error(
                 "not_supported",
                 f"Model {transcription.model!r} is not served by this endpoint "
-                f"(serving {self.server_args.served_model_name!r}); set "
+                f"(serving {get_serving().served_model_name!r}); set "
                 f"transcription.model to null or to the server's model name.",
                 param="session.audio.input.transcription.model",
             )

python/sglang/srt/eplb/eplb_manager.py

@@ -16,7 +16,7 @@ from sglang.srt.eplb.expert_location import (
     get_global_expert_location_metadata,
 )
 from sglang.srt.eplb.expert_location_updater import ExpertLocationUpdater
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_model
 
 if TYPE_CHECKING:
     from sglang.srt.configs.model_config import ModelConfig
@@ -274,8 +274,8 @@ def update_expert_location_with_recovery(
         else:
             # Load the missing weights from disk
             update_weights_from_disk_callable(
-                get_server_args().model_path,
-                get_server_args().load_format,
+                get_model().model_path,
+                get_model().load_format,
                 weight_name_filter=weight_name_filter,
             )
 

python/sglang/srt/eplb/expert_location_dispatch.py

@@ -18,7 +18,7 @@ from typing import Literal, Optional
 import torch
 
 from sglang.srt.eplb.expert_location import get_global_expert_location_metadata
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 
 
 @dataclass
@@ -34,7 +34,7 @@ class ExpertLocationDispatchInfo:
 
     @classmethod
     def init_new(cls, layer_id: int):
-        ep_dispatch_algorithm = get_server_args().ep_dispatch_algorithm
+        ep_dispatch_algorithm = get_exec().moe.ep_dispatch_algorithm
         expert_location_metadata = get_global_expert_location_metadata()
         assert expert_location_metadata is not None
 

python/sglang/srt/eplb/expert_location_updater.py

@@ -26,7 +26,7 @@ from sglang.srt.eplb.expert_location import (
     ExpertLocationMetadata,
     get_global_expert_location_metadata,
 )
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_device
 from sglang.srt.utils import get_bool_env_var
 
 logger = logging.getLogger(__name__)
@@ -107,7 +107,7 @@ def _update_expert_weights_with_canary(
         canary_tensor = (
             _get_canary_value(old_expert_location_metadata, layer_id)
             .clone()
-            .to(device=get_server_args().device, non_blocking=True)
+            .to(device=get_device().device, non_blocking=True)
         )
         routed_experts_weights_of_layer[layer_id].append(canary_tensor)
 

python/sglang/srt/hardware_backend/mlx/model_runner_stub.py

@@ -16,9 +16,8 @@ from sglang.srt.hardware_backend.mlx.kv_cache.auxiliary_state import (
 from sglang.srt.mem_cache.allocator import TokenToKVPoolAllocator
 from sglang.srt.mem_cache.memory_pool import KVCache, ReqToTokenPool
 from sglang.srt.model_executor.model_runner import ModelRunner
-from sglang.srt.model_executor.model_runner_components.layer_setup import (
-    ModelLayerInfo,
-)
+from sglang.srt.model_executor.model_runner_components.layer_setup import ModelLayerInfo
+from sglang.srt.runtime_context import get_exec, get_memory, get_schedule
 
 logger = logging.getLogger(__name__)
 
@@ -144,7 +143,7 @@ class MlxModelRunnerStub(ModelRunner):
         (``MlxAuxiliaryStateComponent``) raises ``NotImplementedError`` for
         the mode.
         """
-        if self.server_args.disable_radix_cache:
+        if get_memory().disable_radix_cache:
             return 1
         return MLX_AUX_STATE_SIZE_MAX_RUNNING_REQUESTS_RATIO
 
@@ -165,7 +164,7 @@ class MlxModelRunnerStub(ModelRunner):
         Requires ``self.max_total_num_tokens`` to already be set.
         """
         capacity_cap = self.max_total_num_tokens // 2
-        requested = self.server_args.max_running_requests
+        requested = get_schedule().max_running_requests
         if requested is None:
             requested_per_worker = None
             resolved = min(capacity_cap, 4096)
@@ -173,7 +172,7 @@ class MlxModelRunnerStub(ModelRunner):
             requested_per_worker = requested // self.dp_size
             resolved = min(requested_per_worker, capacity_cap)
 
-        aux_state_size = self.server_args.max_mamba_cache_size
+        aux_state_size = get_schedule().max_mamba_cache_size
         if (
             mambaish_config(self.model_config) is not None
             and aux_state_size is not None
@@ -209,7 +208,7 @@ class MlxModelRunnerStub(ModelRunner):
         from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter
 
         self.memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=self.server_args.enable_memory_saver
+            enable=get_exec().features.enable_memory_saver
         )
 
         # Load model (sets metadata only)
@@ -241,7 +240,7 @@ class MlxModelRunnerStub(ModelRunner):
 
         # Create minimal pools
         if mambaish_config(self.model_config) is not None:
-            auxiliary_state_size = self.server_args.max_mamba_cache_size
+            auxiliary_state_size = get_schedule().max_mamba_cache_size
             if auxiliary_state_size is None:
                 auxiliary_state_size = (
                     self.max_running_requests * self._aux_state_slots_per_request()
@@ -255,7 +254,7 @@ class MlxModelRunnerStub(ModelRunner):
                 # With the radix cache disabled no tree component exists to
                 # release auxiliary slots, so the pool owns their release
                 # (see MlxAuxiliaryStateReqToTokenPool docstring).
-                owns_auxiliary_state_release=self.server_args.disable_radix_cache,
+                owns_auxiliary_state_release=get_memory().disable_radix_cache,
             )
         else:
             self.req_to_token_pool = ReqToTokenPool(

python/sglang/srt/hardware_backend/mlx/tp_worker.py

@@ -31,6 +31,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardBatch,
     PPProxyTensors,
 )
+from sglang.srt.runtime_context import get_memory, get_model, get_schedule
 
 logger = logging.getLogger(__name__)
 
@@ -47,25 +48,23 @@ class MlxTpModelWorker(TpModelWorker):
     def _init_model_runner(self):
         """Create MLX runner first (auto-sizes pool), then stub with matching size."""
         from sglang.srt.hardware_backend.mlx.model_runner import MlxModelRunner
-        from sglang.srt.hardware_backend.mlx.model_runner_stub import (
-            MlxModelRunnerStub,
-        )
+        from sglang.srt.hardware_backend.mlx.model_runner_stub import MlxModelRunnerStub
 
         logger.info("Initializing MlxModelRunner for end-to-end MLX inference")
         init_kwargs = dict(
-            model_path=self.server_args.model_path,
-            trust_remote_code=self.server_args.trust_remote_code,
-            disable_radix_cache=self.server_args.disable_radix_cache,
-            mem_fraction_static=self.server_args.mem_fraction_static,
-            quantization=self.server_args.quantization,
+            model_path=get_model().model_path,
+            trust_remote_code=get_model().trust_remote_code,
+            disable_radix_cache=get_memory().disable_radix_cache,
+            mem_fraction_static=get_schedule().mem_fraction_static,
+            quantization=get_model().quantization,
         )
-        if self.server_args.max_total_tokens is not None:
-            init_kwargs["pool_size"] = self.server_args.max_total_tokens
+        if get_schedule().max_total_tokens is not None:
+            init_kwargs["pool_size"] = get_schedule().max_total_tokens
         self._mlx_runner = MlxModelRunner(**init_kwargs)
 
         self._model_runner = MlxModelRunnerStub(
             model_config=self.model_config,
-            mem_fraction_static=self.server_args.mem_fraction_static,
+            mem_fraction_static=get_schedule().mem_fraction_static,
             gpu_id=self.gpu_id,
             ps=self.ps,
             nccl_port=self.nccl_port,

python/sglang/srt/hardware_backend/musa/attention/flashattention_backend.py

@@ -19,11 +19,9 @@ from sglang.srt.layers.attention.flashattention_backend import (
     merge_state_v2_wrapper,
 )
 from sglang.srt.layers.radix_attention import AttentionType
-from sglang.srt.layers.utils.cp_utils import (
-    cp_allgather_and_save_kv_cache,
-)
+from sglang.srt.layers.utils.cp_utils import cp_allgather_and_save_kv_cache
 from sglang.srt.mem_cache.memory_pool import KVWriteLoc
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_schedule
 
 if TYPE_CHECKING:
     from sglang.srt.layers.radix_attention import RadixAttention
@@ -515,7 +513,7 @@ class MusaFlashAttentionBackend(FlashAttentionBackend):
                 and not forward_batch.forward_mode.is_draft_extend_v2()
             ):
                 if forward_batch.attn_attend_prefix_cache:
-                    assert not get_server_args().disable_chunked_prefix_cache
+                    assert not get_schedule().disable_chunked_prefix_cache
                     assert forward_batch.prefix_chunk_idx is not None
                     assert forward_batch.prefix_chunk_cu_seq_lens is not None
                     assert forward_batch.prefix_chunk_max_seq_lens is not None

python/sglang/srt/hardware_backend/npu/attention/ascend_dsv4_backend.py

@@ -12,7 +12,7 @@ from sglang.srt.layers.attention.dsv4.compressor import CompressorBackendMixin
 from sglang.srt.layers.attention.dsv4.indexer import C4IndexerBackendMixin
 from sglang.srt.model_executor.forward_batch_info import DSV4OutCacheLoc, ForwardMode
 from sglang.srt.model_executor.forward_context import get_attn_backend
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_parallel, get_spec
 
 if TYPE_CHECKING:
     from sglang.srt.layers.radix_attention import RadixAttention
@@ -1362,9 +1362,8 @@ class DeepseekV4AscendAttnBackend(
             or forward_batch.forward_mode.is_draft_extend_v2()
         ):
             B = forward_batch.batch_size
-            from sglang.srt.runtime_context import get_server_args
 
-            n_draft = get_server_args().speculative_num_draft_tokens or 1
+            n_draft = get_spec().speculative_num_draft_tokens or 1
             actual_q = torch.arange(
                 n_draft, B * n_draft + 1, n_draft, dtype=torch.int32, device=device
             )
@@ -1409,9 +1408,8 @@ class DeepseekV4AscendAttnBackend(
             forward_batch.forward_mode.is_target_verify()
             or forward_batch.forward_mode.is_draft_extend_v2()
         ):
-            from sglang.srt.runtime_context import get_server_args
 
-            max_seqlen_q = get_server_args().speculative_num_draft_tokens or 1
+            max_seqlen_q = get_spec().speculative_num_draft_tokens or 1
         else:
             max_seqlen_q = 1
         return self._kernel_metadata_from_parts(

python/sglang/srt/hardware_backend/npu/graph_runner/vit_npu_graph_runner.py

@@ -27,7 +27,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
 )
 from sglang.srt.layers.attention.vision import VisionAttention
 from sglang.srt.multimodal.vit_cuda_graph_runner import ViTCudaGraphRunner
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_mm
 
 
 class ViTNpuGraphRunner(ViTCudaGraphRunner):
@@ -70,7 +70,7 @@ class ViTNpuGraphRunner(ViTCudaGraphRunner):
         graph = torch_npu.npu.NPUGraph()
         vit = self.vit
 
-        override_backend = get_server_args().mm_attention_backend
+        override_backend = get_mm().mm_attention_backend
         with torch_npu.npu.graph(graph, pool=ViTNpuGraphRunner._graph_memory_pool):
             y = None
             deepstack_outs: List[torch.Tensor] = []

python/sglang/srt/hardware_backend/npu/moe/fuseep.py

@@ -17,7 +17,7 @@ from sglang.srt.environ import envs
 from sglang.srt.hardware_backend.npu.utils import npu_format_cast
 from sglang.srt.layers.moe.token_dispatcher.deepep import DeepEPBuffer
 from sglang.srt.layers.moe.utils import DeepEPMode
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
@@ -57,7 +57,7 @@ def forward_fuseep(
             envs.SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK.get()
         ),
         num_experts=layer.num_experts,
-        fuse_mode=get_server_args().fuseep_mode,
+        fuse_mode=get_exec().moe.fuseep_mode,
     )
     return hidden_states
 
@@ -126,7 +126,7 @@ def process_fuseep_weights(layer: torch.nn.Module, weight_prefix: str) -> None:
 
     Invoked by ``maybe_apply_fuseep_weights`` for both ``"w13"`` and ``"w2"``.
     """
-    if get_server_args().fuseep_mode == 1:
+    if get_exec().moe.fuseep_mode == 1:
         # -- The fused MoE optimization mode "1": dispatch_gmm_combine_decode --
         if weight_prefix == "w13":
             cpu_w13 = layer.w13_weight.data.transpose(1, 2).cpu()
@@ -143,7 +143,7 @@ def process_fuseep_weights(layer: torch.nn.Module, weight_prefix: str) -> None:
             layer.w2_weight_scale = torch.nn.Parameter(
                 w2_scale.to(torch.float32), requires_grad=False
             )
-    elif get_server_args().fuseep_mode == 2:
+    elif get_exec().moe.fuseep_mode == 2:
         # -- The fused MoE optimization mode "2": dispatch_ffn_combine --
         if weight_prefix == "w13":
             w13_weight = _release_weight_cache(layer.w13_weight)

python/sglang/srt/layers/activation.py

@@ -22,9 +22,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from transformers import PretrainedConfig
 
-from sglang.srt.distributed import (
-    divide,
-)
+from sglang.srt.distributed import divide
 from sglang.srt.environ import envs
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.utils import MultiPlatformOp
@@ -33,7 +31,7 @@ from sglang.srt.model_executor.cuda_graph_config import (
     Phase,
     check_cuda_graph_backend,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
@@ -89,7 +87,7 @@ logger = logging.getLogger(__name__)
 class SiluAndMul(MultiPlatformOp):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        if get_server_args().rl_on_policy_target is not None:
+        if get_exec().deterministic.rl_on_policy_target is not None:
             self._forward_method = self.forward_native
         elif _use_aiter and envs.SGLANG_OPT_USE_AITER_SILU_MUL.get():
             self._forward_method = self.forward_aiter

python/sglang/srt/layers/attention/dsa/dsa_indexer.py

@@ -37,10 +37,14 @@ from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph impo
     get_tc_piecewise_forward_context,
     is_in_tc_piecewise_cuda_graph,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
-from sglang.srt.state_capturer.indexer_topk import (
-    maybe_capture_indexer_topk,
+from sglang.srt.runtime_context import (
+    get_device,
+    get_exec,
+    get_parallel,
+    get_schedule,
+    get_server_args,
 )
+from sglang.srt.state_capturer.indexer_topk import maybe_capture_indexer_topk
 from sglang.srt.utils import (
     add_prefix,
     ceil_align,
@@ -105,9 +109,7 @@ if is_npu():
     import torch_npu
     from sglang.srt.hardware_backend.npu.utils import get_indexer_weight_stream
 
-from sglang.srt.distributed import (
-    get_attn_tp_group,
-)
+from sglang.srt.distributed import get_attn_tp_group
 from sglang.srt.distributed.parallel_state import get_pp_group
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.communicator import ScatterMode
@@ -458,7 +460,7 @@ class Indexer(MultiPlatformOp):
             base=rope_theta,  # type: ignore
             rope_scaling=rope_scaling,
             is_neox_style=is_neox_style,
-            device=get_server_args().device,
+            device=get_device().device,
         )
         self.block_size = block_size
         self.scale_fmt = scale_fmt
@@ -469,7 +471,7 @@ class Indexer(MultiPlatformOp):
             self.num_local_tokens = getattr(config, "index_local_tokens", 0)
 
         self.paged_mqa_logits_backend = DSAPagedMQALogitsBackend.resolve(
-            get_server_args().dsa_paged_mqa_logits_backend
+            get_exec().kernel.dsa_paged_mqa_logits_backend
         )
 
     @contextlib.contextmanager
@@ -1055,7 +1057,7 @@ class Indexer(MultiPlatformOp):
         total_mem = torch.cuda.get_device_properties(device_index).total_memory
 
         total_mem_budget = int(total_mem * self._MQA_LOGITS_TOTAL_MEM_FRACTION)
-        mem_fraction_static = get_server_args().mem_fraction_static
+        mem_fraction_static = get_schedule().mem_fraction_static
         if mem_fraction_static is None:
             static_budget = total_mem_budget
         else:

python/sglang/srt/layers/attention/dsv4/indexer.py

@@ -28,16 +28,14 @@ from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph.context
 from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
     is_in_tc_piecewise_cuda_graph,
 )
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.state_capturer.indexer_topk import get_global_indexer_capturer
 from sglang.srt.utils import add_prefix, is_cuda, is_hip, is_xpu
 from sglang.srt.utils.common import is_sm120_supported
 
 if TYPE_CHECKING:
     from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
-    from sglang.srt.layers.attention.dsv4.compressor import (
-        CompressorBackendMixin,
-    )
+    from sglang.srt.layers.attention.dsv4.compressor import CompressorBackendMixin
     from sglang.srt.layers.quantization import QuantizationConfig
     from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
     from sglang.srt.model_executor.forward_batch_info import ForwardBatch
@@ -129,9 +127,7 @@ def _aiter_fp8_paged_mqa_logits(
     clean_logits: bool = False,
 ) -> torch.Tensor:
     """Wrapper adapting aiter's deepgemm_fp8_paged_mqa_logits to SGLang's interface."""
-    from aiter.ops.triton.attention.pa_mqa_logits import (
-        deepgemm_fp8_paged_mqa_logits,
-    )
+    from aiter.ops.triton.attention.pa_mqa_logits import deepgemm_fp8_paged_mqa_logits
 
     batch_size = q_fp8.shape[0]
     next_n = q_fp8.shape[1]
@@ -838,9 +834,8 @@ class C4Indexer(nn.Module):
         self.rotary_emb = rotary_emb
         self.freqs_cis = freqs_cis
         self.weight_scale: float = self.softmax_scale * self.n_heads**-0.5
-        from sglang.srt.runtime_context import get_server_args
 
-        self.use_fp4_indexer = get_server_args().enable_deepseek_v4_fp4_indexer
+        self.use_fp4_indexer = get_exec().kernel.enable_deepseek_v4_fp4_indexer
         self.alt_streams = alt_streams
 
     def compute_q(

python/sglang/srt/layers/attention/flashattention_backend.py

@@ -13,9 +13,7 @@ from sglang.kernels.ops.attention.metadata import (
 )
 from sglang.kernels.ops.attention.pa_page_table import _build_pa_page_table
 from sglang.kernels.ops.attention.utils import assert_buffer_fits
-from sglang.kernels.ops.kvcache.trtllm_mha_page_table import (
-    build_trtllm_mha_page_table,
-)
+from sglang.kernels.ops.kvcache.trtllm_mha_page_table import build_trtllm_mha_page_table
 from sglang.srt.configs.model_config import AttentionArch
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.cp.base import CPAttentionBackendKind, get_cp_strategy
@@ -28,7 +26,7 @@ from sglang.srt.layers.utils.cp_utils import (
 from sglang.srt.mem_cache.memory_pool import KVWriteLoc
 from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.speculative.ragged_verify import build_ragged_target_verify_geometry
 from sglang.srt.speculative.spec_info import SpecInput, SpeculativeAlgorithm
 from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
@@ -166,9 +164,12 @@ class FlashAttentionBackend(AttentionBackend):
         self.token_to_kv_pool = model_runner.token_to_kv_pool
         self.req_to_token = model_runner.req_to_token_pool.req_to_token
         self.kv_cache_dtype = model_runner.kv_cache_dtype
-        from sglang.srt.runtime_context import get_model
 
-        self.kv_cache_dtype_str = get_model().kv_cache_dtype
+        self.kv_cache_dtype_str = getattr(
+            model_runner,
+            "kv_cache_dtype_str",
+            model_runner.server_args.kv_cache_dtype,
+        )
         self.kv_cache_is_mxfp8 = self.kv_cache_dtype_str == "mxfp8"
         self.page_size = model_runner.page_size
         # Static page-table width (upper bound). The device-side page-table build
@@ -1479,7 +1480,7 @@ class FlashAttentionBackend(AttentionBackend):
             ):
                 # Do multi-head attention with chunked prefix cache
                 if forward_batch.attn_attend_prefix_cache:
-                    assert not get_server_args().disable_chunked_prefix_cache
+                    assert not get_schedule().disable_chunked_prefix_cache
                     # MHA for chunked prefix kv cache when running model with MLA
                     assert forward_batch.prefix_chunk_idx is not None
                     assert forward_batch.prefix_chunk_cu_seq_lens is not None

python/sglang/srt/layers/attention/flashinfer_mla_backend.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_disagg, get_exec, get_parallel, get_schedule
 
 """
 Support attention backend for flashinfer MLA.
@@ -32,7 +32,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMo
 from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
     is_in_tc_piecewise_cuda_graph,
 )
-from sglang.srt.runtime_context import get_buffer, get_server_args
+from sglang.srt.runtime_context import get_buffer
 from sglang.srt.speculative.spec_info import SpecInput
 from sglang.srt.speculative.spec_utils import (
     draft_kv_indices_buffer_width,
@@ -223,9 +223,9 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         self.token_to_kv_pool = model_runner.token_to_kv_pool
         self.enable_chunk_kv = (
             not skip_prefill
-            and get_server_args().disaggregation_mode != "decode"
-            and not get_server_args().disable_chunked_prefix_cache
-            and not get_server_args().flashinfer_mla_disable_ragged
+            and get_disagg().disaggregation_mode != "decode"
+            and not get_schedule().disable_chunked_prefix_cache
+            and not get_exec().kernel.flashinfer_mla_disable_ragged
         )
         self.page_size = model_runner.page_size
 
@@ -401,7 +401,7 @@ class FlashInferMLAAttnBackend(AttentionBackend):
             prefix_lens = forward_batch.extend_prefix_lens
             extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
             use_ragged = (
-                not get_server_args().flashinfer_mla_disable_ragged
+                not get_exec().kernel.flashinfer_mla_disable_ragged
                 and extend_no_prefix
                 # Piecewise cuda graph should use paged prefill to be compatible with prefix cache
                 and not is_in_tc_piecewise_cuda_graph()

python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py

@@ -19,7 +19,7 @@ from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.model_executor.model_runner import ModelRunner
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec, get_memory, get_server_args
 from sglang.srt.speculative.eagle_info import EagleDraftInput, EagleVerifyInput
 from sglang.srt.speculative.spec_info import SpecInput
 
@@ -350,7 +350,7 @@ class MambaAttnBackendBase(AttentionBackend):
         """Per-row (length bs) bool flush mask = the radix track's seq_lens_cpu %
         mamba_track_interval == 0, so force-flush and snapshot fire on the same
         steps (no off-by-one)."""
-        interval = get_server_args().mamba_track_interval
+        interval = get_exec().mamba.mamba_track_interval
         if seq_lens_cpu is None:
             # Should not happen for the supported config; stay safe and never flush.
             return torch.zeros((bs,), dtype=torch.bool)
@@ -764,7 +764,7 @@ class Mamba2AttnBackend(MambaAttnBackendBase):
         # Page-major stores state strided; only the stride-aware Triton causal-conv
         # reads it (CUDA causal_conv1d garbles it). A model may also force Triton.
         use_triton_causal_conv = (
-            use_triton_causal_conv or get_server_args().enable_page_major_kv_layout
+            use_triton_causal_conv or get_memory().enable_page_major_kv_layout
         )
         layer_cache = self.req_to_token_pool.mamba2_layer_cache(layer_id)
         mixer_out, intermediate_states = mixer.forward(

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -38,7 +38,11 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMo
 from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
     is_in_tc_piecewise_cuda_graph,
 )
-from sglang.srt.runtime_context import get_buffer, get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_buffer,
+    get_parallel,
+    get_schedule,
+)
 from sglang.srt.utils import is_flashinfer_available, is_float4_e2m1fn_x2
 
 if is_flashinfer_available():
@@ -197,9 +201,7 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
         self.forward_prefill_metadata: Optional[TRTLLMMLAPrefillMetadata] = None
         self.forward_decode_metadata: Union[TRTLLMMLADecodeMetadata, None] = None
 
-        self.disable_chunked_prefix_cache = (
-            get_server_args().disable_chunked_prefix_cache
-        )
+        self.disable_chunked_prefix_cache = get_schedule().disable_chunked_prefix_cache
 
         self.num_draft_tokens = model_runner.server_args.speculative_num_draft_tokens
         self.cuda_graph_custom_mask = None

python/sglang/srt/layers/attention/vision.py

@@ -15,7 +15,7 @@ from einops import rearrange
 from sglang.jit_kernel.norm import can_use_fused_inplace_qknorm as can_use_jit_qk_norm
 from sglang.srt.environ import envs
 from sglang.srt.models.utils import apply_qk_norm
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_exec, get_mm, get_parallel
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
@@ -69,9 +69,7 @@ if _is_npu:
 if _is_xpu:
     from sgl_kernel.flash_attn import flash_attn_varlen_func
 
-from sglang.kernels.ops.attention.prefill_attention import (
-    context_attention_fwd,
-)
+from sglang.kernels.ops.attention.prefill_attention import context_attention_fwd
 from sglang.srt.distributed import (
     split_tensor_along_last_dim,
     tensor_model_parallel_all_gather,
@@ -86,7 +84,6 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.quantization import QuantizationConfig
 from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb
 from sglang.srt.layers.rotary_embedding.utils import apply_rotary_pos_emb_native_eager
-from sglang.srt.runtime_context import get_server_args
 from sglang.srt.utils import add_prefix
 
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
@@ -1045,7 +1042,7 @@ class VisionAttention(nn.Module):
         # Select attention backend via a unified method
         _passed_backend = qkv_backend
         qkv_backend = self._determine_attention_backend(_passed_backend)
-        if get_server_args().mm_attention_backend is None and _passed_backend is None:
+        if get_mm().mm_attention_backend is None and _passed_backend is None:
             print_info_once(f"Multimodal attention backend not set. Use {qkv_backend}.")
         print_info_once(f"Using {qkv_backend} as multimodal attention backend.")
 
@@ -1124,7 +1121,7 @@ class VisionAttention(nn.Module):
                 weight_dtype=torch.float32,
                 cast_x_before_out_mul=True,
             )
-            if get_server_args().rl_on_policy_target is not None
+            if get_exec().deterministic.rl_on_policy_target is not None
             else {}
         )
         q_norm = RMSNorm(
@@ -1152,7 +1149,7 @@ class VisionAttention(nn.Module):
         - CUDA (other): "triton_attn"
         - Non-CUDA: "sdpa"
         """
-        override_backend = get_server_args().mm_attention_backend
+        override_backend = get_mm().mm_attention_backend
         if override_backend is not None:
             backend = override_backend
         elif passed_backend is not None:
@@ -1257,7 +1254,7 @@ class VisionAttention(nn.Module):
             x = x.unsqueeze(0)
         assert x.dim() == 3, x.shape
         if (
-            get_server_args().rl_on_policy_target is not None
+            get_exec().deterministic.rl_on_policy_target is not None
             and position_embeddings is not None
         ):
             assert isinstance(position_embeddings, tuple), (

python/sglang/srt/layers/attention/xpu_backend.py

@@ -15,7 +15,7 @@ from sglang.srt.layers.attention.flashattention_backend import (
 from sglang.srt.mem_cache.memory_pool import KVWriteLoc
 from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_schedule
 
 if TYPE_CHECKING:
     from sglang.srt.layers.radix_attention import RadixAttention
@@ -69,9 +69,12 @@ class XPUAttentionBackend(AttentionBackend):
         self.token_to_kv_pool = model_runner.token_to_kv_pool
         self.req_to_token = model_runner.req_to_token_pool.req_to_token
         self.kv_cache_dtype = model_runner.kv_cache_dtype
-        from sglang.srt.runtime_context import get_model
 
-        self.kv_cache_dtype_str = get_model().kv_cache_dtype
+        self.kv_cache_dtype_str = getattr(
+            model_runner,
+            "kv_cache_dtype_str",
+            model_runner.server_args.kv_cache_dtype,
+        )
         self.page_size = model_runner.page_size
         self.use_mla = model_runner.model_config.attention_arch == AttentionArch.MLA
         self.skip_prefill = skip_prefill
@@ -640,7 +643,7 @@ class XPUAttentionBackend(AttentionBackend):
             ):
                 # Do multi-head attention with chunked prefix cache
                 if forward_batch.attn_attend_prefix_cache:
-                    assert not get_server_args().disable_chunked_prefix_cache
+                    assert not get_schedule().disable_chunked_prefix_cache
                     # MHA for chunked prefix kv cache when running model with MLA
                     assert forward_batch.prefix_chunk_idx is not None
                     assert forward_batch.prefix_chunk_cu_seq_lens is not None

python/sglang/srt/layers/communicator.py

@@ -72,7 +72,13 @@ from sglang.srt.model_executor.cuda_graph_config import (
     check_cuda_graph_backend,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.runtime_context import get_forward, get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_exec,
+    get_forward,
+    get_parallel,
+    get_server_args,
+    get_spec,
+)
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
     get_bool_env_var,
@@ -170,7 +176,7 @@ def apply_flashinfer_allreduce_fusion(batch_size: int):
         and batch_size > 0
         and batch_size <= FUSE_ALLREDUCE_MAX_BATCH_SIZE
         and not is_dp_attention_enabled()
-        and get_server_args().flashinfer_allreduce_fusion_backend is not None
+        and get_exec().comm.flashinfer_allreduce_fusion_backend is not None
         and not is_flashinfer_allreduce_unavailable()
     )
 
@@ -186,7 +192,7 @@ def apply_aiter_all_reduce_fusion(input_tensor: torch.Tensor):
         and total_bytes <= 8 * 1024 * 8192
         and get_parallel().tp_size != 6
         and not is_dp_attention_enabled()
-        and get_server_args().enable_aiter_allreduce_fusion
+        and get_exec().comm.enable_aiter_allreduce_fusion
     )
 
 
@@ -274,7 +280,7 @@ class AttnTpContext:
             and get_moe_a2a_backend().is_none()
             and not enable_moe_dense_fully_dp()
             and not check_cuda_graph_backend(Phase.PREFILL, Backend.TC_PIECEWISE)
-            and get_server_args().speculative_algorithm != "EAGLE3"
+            and get_spec().speculative_algorithm != "EAGLE3"
         )
         if get_server_args().enable_attn_tp_input_scattered:
             if not self.allow_input_scattered:
@@ -407,7 +413,7 @@ class LayerScatterModes:
             not context.is_layer_sparse
             and context.is_next_layer_sparse
             and enable_moe_dense_fully_dp()
-            and get_server_args().enable_two_batch_overlap
+            and get_exec().overlap.enable_two_batch_overlap
         )
 
     @classmethod
@@ -467,7 +473,7 @@ class LayerCommunicator:
         )
         self._post_init_communicate()
         self._speculative_algo = SpeculativeAlgorithm.from_string(
-            get_server_args().speculative_algorithm
+            get_spec().speculative_algorithm
         )
 
     def _post_init_communicate(self):
@@ -815,7 +821,7 @@ class LayerCommunicator:
                     and get_parallel().tp_size != 6
                     and not is_dp_attention_enabled()
                     and get_moe_a2a_backend().is_none()
-                    and get_server_args().enable_aiter_allreduce_fusion
+                    and get_exec().comm.enable_aiter_allreduce_fusion
                 )
             )
             and (not self.is_last_layer)
@@ -1120,7 +1126,7 @@ class CommunicateWithAllReduceAndLayerNormFn:
             if not handled:
                 quantize_communications = (
                     not forward_batch.forward_mode.is_decode_or_idle()
-                    and get_server_args().enable_quant_communications
+                    and get_exec().comm.enable_quant_communications
                 )
                 if quantize_communications:
                     hidden_states = attention_tensor_model_parallel_quant_all_reduce(

python/sglang/srt/layers/cp/zigzag.py

@@ -48,12 +48,10 @@ from sglang.srt.layers.cp.base import (
     CPAttentionBackendKind,
 )
 from sglang.srt.layers.cp.padding import pad_local_rows
-from sglang.srt.layers.dp_attention import (
-    is_allocation_symmetric,
-)
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.mem_cache.memory_pool import KVWriteLoc
 from sglang.srt.model_executor.forward_context import get_token_to_kv_pool
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_device, get_parallel
 
 
 @dataclass
@@ -208,10 +206,8 @@ class ZigzagCPStrategy(ContextParallelStrategy):
             actual_seq_q_prev_list.append(block_sizes[cp_rank])
             actual_seq_q_next_list.append(block_sizes[cp_segment_num - cp_rank - 1])
 
-        from sglang.srt.runtime_context import get_server_args
-
         try:
-            device = torch.device(get_server_args().device)
+            device = torch.device(get_device().device)
         except Exception:
             device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         cu_prev = [0] + list(accumulate(actual_seq_q_prev_list))

python/sglang/srt/layers/dcp/planner.py

@@ -26,7 +26,7 @@ from sglang.kernels.ops.attention.dcp_kernels import (
 )
 from sglang.srt.layers.dcp.layout import update_local_kv_lens_for_dcp
 from sglang.srt.layers.dcp.metadata import DecodeContextParallelMetadata
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_device, get_parallel
 
 
 def prepare_decode_context_parallel_metadata(
@@ -53,12 +53,12 @@ def prepare_decode_context_parallel_metadata(
     extend_prefix_starts = torch.zeros(
         len(seq_lens),
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
     extend_cu_prefix_lens = torch.zeros(
         len(seq_lens) + 1,
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
     extend_cu_prefix_lens[1:] = torch.cumsum(extend_prefix_lens, dim=0)
     extend_cu_prefix_lens = extend_cu_prefix_lens[:-1]
@@ -67,7 +67,7 @@ def prepare_decode_context_parallel_metadata(
     dcp_prefix_kv_indices = torch.empty(
         sum(extend_prefix_lens_cpu),
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
     create_chunked_prefix_cache_kv_indices_fn[(len(seq_lens),)](
         req_to_token,
@@ -81,20 +81,20 @@ def prepare_decode_context_parallel_metadata(
     dcp_kv_indptr = torch.zeros(
         len(seq_lens) + 1,
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
     dcp_kv_indptr[1:] = seq_lens.cumsum(dim=0)
     dcp_kv_indptr = dcp_kv_indptr[: (len(seq_lens) + 1)]
     dcp_kv_indices = torch.zeros(
         seq_lens_sum,
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
 
     extend_cu_lens = torch.zeros(
         len(seq_lens) + 1,
         dtype=torch.int32,
-        device=get_server_args().device,
+        device=get_device().device,
     )
     extend_cu_lens[1:] = torch.cumsum(extend_seq_lens, dim=0)
     extend_cu_lens = extend_cu_lens[:-1]

python/sglang/srt/layers/layernorm.py

@@ -31,7 +31,7 @@ from sglang.srt.model_executor.cuda_graph_config import (
     Phase,
     check_cuda_graph_backend,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
@@ -130,9 +130,7 @@ if _is_cuda:
     # BEFORE the weight multiply, so the multiply is done in the narrow dtype.
     _jit_rmsnorm_hf_available = False
     try:
-        from sglang.jit_kernel.rmsnorm_hf import (
-            is_supported_rmsnorm_hf_hidden_size,
-        )
+        from sglang.jit_kernel.rmsnorm_hf import is_supported_rmsnorm_hf_hidden_size
         from sglang.jit_kernel.rmsnorm_hf import rmsnorm_hf as _jit_rmsnorm_hf
 
         _jit_rmsnorm_hf_available = True
@@ -144,9 +142,7 @@ if _is_cuda:
         _jit_rmsnorm_hf = None
 
     from sglang.jit_kernel.norm import fused_add_rmsnorm as _jit_fused_add_rmsnorm
-    from sglang.jit_kernel.norm import (
-        is_supported_jit_fused_add_rmsnorm_hidden_size,
-    )
+    from sglang.jit_kernel.norm import is_supported_jit_fused_add_rmsnorm_hidden_size
 
 
 logger = logging.getLogger(__name__)
@@ -206,7 +202,7 @@ def _forward_with_allreduce_fusion(
                     return fused_result
 
             # For AITER route, preserve correctness when fused path is unavailable.
-            if _use_aiter and get_server_args().enable_aiter_allreduce_fusion:
+            if _use_aiter and get_exec().comm.enable_aiter_allreduce_fusion:
                 x = tensor_model_parallel_all_reduce(x)
                 return norm_module.forward(x, residual, None)
 
@@ -284,7 +280,7 @@ class RMSNorm(MultiPlatformOp):
             if (
                 residual is not None
                 or self.cast_x_before_out_mul
-                or get_server_args().rl_on_policy_target == "fsdp"
+                or get_exec().deterministic.rl_on_policy_target == "fsdp"
             ):
                 return self.forward_native(x, residual, post_residual_addition)
             out = rms_norm_batch_invariant(
@@ -391,7 +387,7 @@ class RMSNorm(MultiPlatformOp):
             if (
                 residual is not None
                 or self.cast_x_before_out_mul
-                or get_server_args().rl_on_policy_target == "fsdp"
+                or get_exec().deterministic.rl_on_policy_target == "fsdp"
                 or (self._fused_pad_kernel is not None and self.x_pad_to_multiple > 0)
             ):
                 return self.forward_native(x, residual, post_residual_addition)
@@ -452,7 +448,7 @@ class RMSNorm(MultiPlatformOp):
             if (
                 residual is not None
                 or self.cast_x_before_out_mul
-                or get_server_args().rl_on_policy_target == "fsdp"
+                or get_exec().deterministic.rl_on_policy_target == "fsdp"
             ):
                 return self.forward_native(x, residual, post_residual_addition)
             return rms_norm_batch_invariant(
@@ -579,7 +575,10 @@ class RMSNorm(MultiPlatformOp):
         if self.variance_size_override is not None:
             return self.forward_native(x, residual, post_residual_addition)
         if is_batch_invariant_mode_enabled():
-            if residual is not None or get_server_args().rl_on_policy_target == "fsdp":
+            if (
+                residual is not None
+                or get_exec().deterministic.rl_on_policy_target == "fsdp"
+            ):
                 return self.forward_native(x, residual, post_residual_addition)
             return rms_norm_batch_invariant(
                 x,

python/sglang/srt/layers/linear.py

@@ -25,9 +25,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
 from sglang.srt.environ import envs
-from sglang.srt.layers.dp_attention import (
-    is_allocation_symmetric,
-)
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe.utils import should_skip_mlp_all_reduce
 from sglang.srt.layers.parameter import (
     BasevLLMParameter,
@@ -39,7 +37,7 @@ from sglang.srt.layers.parameter import (
     _ColumnvLLMParameter,
 )
 from sglang.srt.layers.utils import pad_or_narrow_weight
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.utils import get_bool_env_var, is_cpu, is_hip, is_npu, set_weight_attrs
 
 if TYPE_CHECKING:
@@ -759,9 +757,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             shard_offsets.append((i, current_shard_offset, output_size))
             current_shard_offset += output_size
         if _is_cpu:
-            from sglang.srt.model_loader.weight_utils import (
-                pad_loaded_weight,
-            )
+            from sglang.srt.model_loader.weight_utils import pad_loaded_weight
 
             loaded_weight = pad_loaded_weight(
                 loaded_weight, param.output_dim, output_sizes
@@ -805,9 +801,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             current_block_offset += shard_block_size
 
         if _is_cpu:
-            from sglang.srt.model_loader.weight_utils import (
-                pad_loaded_weight,
-            )
+            from sglang.srt.model_loader.weight_utils import pad_loaded_weight
 
             loaded_weight = pad_loaded_weight(
                 loaded_weight, param.output_dim, shard_block_sizes
@@ -1596,7 +1590,7 @@ class RowParallelLinear(LinearBase):
                 quantize_communications = (
                     (
                         not forward_batch.forward_mode.is_decode_or_idle()
-                        and get_server_args().enable_quant_communications
+                        and get_exec().comm.enable_quant_communications
                     )
                     if forward_batch is not None
                     else False

python/sglang/srt/layers/logits_processor.py

@@ -47,7 +47,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardBatch,
     ForwardMode,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args
 from sglang.srt.utils.common import (
     is_cpu,
     is_npu,
@@ -346,7 +346,7 @@ class LogitsProcessor(nn.Module):
         self.vocab_size = config.vocab_size
         self.logit_scale = logit_scale
         self.use_attn_tp_group = get_server_args().enable_dp_lm_head
-        self.use_fp32_lm_head = get_server_args().enable_fp32_lm_head
+        self.use_fp32_lm_head = get_exec().features.enable_fp32_lm_head
         if self.use_attn_tp_group:
             self.attn_tp_size = get_parallel().attn_tp_size
             self.do_tensor_parallel_all_gather = (
@@ -370,8 +370,8 @@ class LogitsProcessor(nn.Module):
             self.final_logit_softcapping = None
 
         self.return_full_logits = return_full_logits
-        self.enable_mis = get_server_args().enable_mis
-        self.rl_on_policy_target = get_server_args().rl_on_policy_target
+        self.enable_mis = get_exec().features.enable_mis
+        self.rl_on_policy_target = get_exec().deterministic.rl_on_policy_target
 
         self._logits_gatherer = triton_symm_mem_ag.MultimemAllGatherer(
             max_tokens=triton_symm_mem_ag.recommended_max_tokens(

python/sglang/srt/layers/moe/hash_topk.py

@@ -7,9 +7,7 @@ import torch
 from torch import nn
 
 from sglang.srt.environ import envs
-from sglang.srt.eplb.expert_distribution import (
-    get_global_expert_distribution_recorder,
-)
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.eplb.expert_location_dispatch import (
     ExpertLocationDispatchInfo,
     topk_ids_logical_to_physical,
@@ -22,6 +20,7 @@ from sglang.srt.layers.moe.topk import (
     remap_topk_for_per_rank_shared_slots,
 )
 from sglang.srt.layers.moe.utils import has_per_rank_fused_shared_slots
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import is_hip, is_npu
 
 logger = logging.getLogger(__name__)
@@ -44,10 +43,9 @@ class HashTopK(nn.Module):
     ):
         super().__init__()
         self.layer_id = layer_id
-        from sglang.srt.runtime_context import get_server_args
 
         self.enable_waterfill = (
-            num_fused_shared_experts > 0 and get_server_args().enable_waterfill
+            num_fused_shared_experts > 0 and get_exec().moe.enable_waterfill
         )
         self.waterfill_balancer = None
 

python/sglang/srt/layers/moe/moe_runner/triton_utils/fused_moe.py

@@ -28,7 +28,7 @@ from sglang.srt.environ import envs
 from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
 from sglang.srt.layers.moe.utils import get_moe_padding_size
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
@@ -506,7 +506,7 @@ def _fused_moe_kernel_sequence(
             out_hidden_states = torch.empty_like(hidden_states)
 
     use_fused_moe_sum_all_reduce = (
-        get_server_args().enable_fused_moe_sum_all_reduce
+        get_exec().moe.enable_fused_moe_sum_all_reduce
         and (not no_combine)
         and (topk > 2)
         and (not use_int8_w8a16)

python/sglang/srt/layers/moe/moe_runner/triton_utils/fused_moe_triton_config.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, List, Optional, Tuple
 import torch
 import triton
 
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import get_device_name, is_hip
 
 logger = logging.getLogger(__name__)
@@ -69,7 +69,7 @@ def get_moe_configs(
     kernel on a given batch size bs, the closest batch size in the grid should
     be picked and the associated configuration chosen to invoke the kernel.
     """
-    if get_server_args().enable_deterministic_inference:
+    if get_exec().deterministic.enable_deterministic_inference:
         logger.warning(
             "Deterministic inference is enabled, using default MoE kernel config."
         )
@@ -187,7 +187,7 @@ def get_default_config(
     is_marlin: bool,
     block_shape: Optional[List[int]] = None,
 ) -> Dict[str, int]:
-    if get_server_args().enable_deterministic_inference:
+    if get_exec().deterministic.enable_deterministic_inference:
         config = {
             "BLOCK_SIZE_M": 64,
             "BLOCK_SIZE_N": 64,

python/sglang/srt/layers/moe/token_dispatcher/flashinfer.py

@@ -21,13 +21,9 @@ from sglang.srt.layers.moe.token_dispatcher import (
 from sglang.srt.layers.moe.token_dispatcher.flashinfer_utils import (
     TorchDistributedCommBackend,
 )
-from sglang.srt.layers.moe.topk import (
-    StandardTopKOutput,
-    TopKOutput,
-    TopKOutputChecker,
-)
+from sglang.srt.layers.moe.topk import StandardTopKOutput, TopKOutput, TopKOutputChecker
 from sglang.srt.layers.moe.utils import get_moe_runner_backend
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_schedule, get_spec
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import get_int_env_var
 
@@ -123,7 +119,7 @@ class FlashinferDispatcher(BaseDispatcher):
         # max_running_requests is not yet resolved at model-construction time,
         # so we use 4096 as a floor to cover decode batches and _dummy_run
         # (which warms up at batch_size = req_to_token_pool.size).
-        cps = get_server_args().chunked_prefill_size
+        cps = get_schedule().chunked_prefill_size
         default_max_tokens = max(cps if cps and cps > 0 else 4096, 4096)
         self.max_num_tokens = get_int_env_var(
             "SGLANG_FLASHINFER_NUM_MAX_DISPATCH_TOKENS_PER_RANK",
@@ -132,7 +128,7 @@ class FlashinferDispatcher(BaseDispatcher):
 
         # Calculate workspace size. For eagle mode, use the larger workspace size since nextn layer will be unquantized.
         speculative_algo = SpeculativeAlgorithm.from_string(
-            get_server_args().speculative_algorithm
+            get_spec().speculative_algorithm
         )
         if MOE_NVFP4_DISPATCH and not speculative_algo.is_eagle():
             total_dispatch_payload_size_per_token = (

python/sglang/srt/layers/moe/topk.py

@@ -32,7 +32,7 @@ from typing import (
 import torch
 import torch.nn.functional as F
 
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_exec, get_lora, get_parallel
 
 try:
     from triton_kernels.matmul_ogs import GatherIndx, RoutingData, ScatterIndx
@@ -83,9 +83,7 @@ except ImportError:
     pass
 
 from sglang.jit_kernel.dsv4 import mask_topk_ids
-from sglang.srt.distributed import (
-    get_tp_group,
-)
+from sglang.srt.distributed import get_tp_group
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
@@ -98,9 +96,7 @@ from sglang.srt.eplb.expert_location_dispatch import (
 )
 from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe import get_moe_runner_backend
-from sglang.srt.layers.moe.utils import (
-    has_per_rank_fused_shared_slots,
-)
+from sglang.srt.layers.moe.utils import has_per_rank_fused_shared_slots
 from sglang.srt.layers.utils import MultiPlatformOp
 from sglang.srt.state_capturer.routed_experts import get_global_experts_capturer
 from sglang.srt.utils import (
@@ -419,10 +415,9 @@ class TopK(MultiPlatformOp):
             assert num_expert_group is not None and topk_group is not None
 
         self.layer_id = layer_id
-        from sglang.srt.runtime_context import get_server_args
 
         self.enable_waterfill = (
-            num_fused_shared_experts > 0 and get_server_args().enable_waterfill
+            num_fused_shared_experts > 0 and get_exec().moe.enable_waterfill
         )
 
         self.waterfill_balancer = None
@@ -496,9 +491,8 @@ class TopK(MultiPlatformOp):
         # ===== TO BE REFACTORED ====
         elif get_moe_runner_backend().is_experimental_sgl_trtllm():
             try:
-                from sglang.srt.runtime_context import get_server_args
 
-                use_standard_for_lora = bool(get_server_args().enable_lora)
+                use_standard_for_lora = bool(get_lora().enable_lora)
             except ValueError:
                 use_standard_for_lora = False
             output_format = (

python/sglang/srt/layers/quantization/fp8_utils.py

@@ -13,7 +13,7 @@ from sglang.kernels.ops.quantization.fp8_kernel import (
 )
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.quantization.mxfp4_tensor import MXFP4QuantizeUtil
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.utils.common import torch_release
 
 if TYPE_CHECKING:
@@ -34,7 +34,6 @@ from sglang.kernels.ops.quantization.fp8_kernel import (
     w8a8_block_fp8_matmul_deepgemm,
     w8a8_block_fp8_matmul_triton,
 )
-from sglang.srt.runtime_context import get_server_args
 from sglang.srt.utils import (
     ceil_align,
     ceil_div,
@@ -1470,9 +1469,7 @@ def requant_block_scale_ue8m0_for_deepgemm(
     scales are not already UE8M0, and DeepGEMM can run the layer (bf16 output,
     aligned shape). Returns True when it requantizes.
     """
-    from sglang.srt.model_loader.utils import (
-        should_deepgemm_weight_requant_ue8m0,
-    )
+    from sglang.srt.model_loader.utils import should_deepgemm_weight_requant_ue8m0
 
     if (
         not use_deepgemm_runner
@@ -1794,7 +1791,7 @@ def apply_fp8_linear(
         if (
             input_scale is not None
             and input_scale.numel() == 1
-            and get_server_args().cuda_graph_config.prefill.tc_compiler == "inductor"
+            and get_exec().graph.cuda_graph_config.prefill.tc_compiler == "inductor"
         ):
             qinput = (
                 (input_2d * input_scale.reciprocal())

python/sglang/srt/layers/quantization/mxfp4.py

@@ -48,7 +48,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizeMethodBase,
 )
 from sglang.srt.layers.quantization.utils import is_layer_skipped
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     cpu_has_amx_support,
     is_cpu,
@@ -77,9 +77,7 @@ if is_flashinfer_available():
         nvfp4_block_scale_interleave,
         trtllm_fp4_block_scale_moe,
     )
-    from flashinfer.fused_moe.core import (
-        get_w2_permute_indices_with_cache,
-    )
+    from flashinfer.fused_moe.core import get_w2_permute_indices_with_cache
 
     # SM90 mixed-input helpers landed in FlashInfer #3084 (post-0.6.10). Older
     # versions don't ship them; gate at import so unrelated code paths still load.
@@ -334,7 +332,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.use_flashinfer = get_moe_runner_backend().is_flashinfer_mxfp4()
         self.use_marlin = get_moe_runner_backend().is_marlin()
         self.flashinfer_mxfp4_moe_precision = (
-            get_server_args().flashinfer_mxfp4_moe_precision
+            get_exec().moe.flashinfer_mxfp4_moe_precision
         )
         # When `flashinfer_mxfp4` is enabled, dispatch to one of two FlashInfer
         # entry points depending on the GPU:

python/sglang/srt/layers/quantization/mxfp4_flashinfer_trtllm_moe.py

@@ -14,7 +14,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
 )
 from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe.utils import RoutingMethodType
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     is_flashinfer_available,
     log_info_on_rank0,
@@ -51,7 +51,7 @@ class Mxfp4FlashinferTrtllmMoEMethod:
         self._fp8 = fp8_method
         self.prefix = prefix
         self.flashinfer_mxfp4_moe_precision = (
-            get_server_args().flashinfer_mxfp4_moe_precision
+            get_exec().moe.flashinfer_mxfp4_moe_precision
         )
 
     def create_moe_runner(self, layer, moe_runner_config):
@@ -376,9 +376,7 @@ def maybe_fuse_routed_scale_and_shared_add(
     from sglang.srt.layers.quantization.mxfp4_flashinfer_cutlass_moe import (
         Mxfp4FlashinferCutlassMoEMethod,
     )
-    from sglang.srt.layers.quantization.mxfp4_marlin_moe import (
-        Mxfp4MarlinMoEMethod,
-    )
+    from sglang.srt.layers.quantization.mxfp4_marlin_moe import Mxfp4MarlinMoEMethod
 
     fused = isinstance(
         experts.quant_method,

python/sglang/srt/layers/rotary_embedding/base.py

@@ -11,7 +11,7 @@ from sglang.srt.environ import envs
 from sglang.srt.layers.rotary_embedding.utils import apply_rotary_emb
 from sglang.srt.layers.utils import MultiPlatformOp
 from sglang.srt.platforms import current_platform
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
@@ -65,9 +65,7 @@ if _is_npu:
         )
 
 if _is_hip:
-    from sglang.kernels.ops.attention.utils import (
-        fused_qk_rope_reshape_and_cache,
-    )
+    from sglang.kernels.ops.attention.utils import fused_qk_rope_reshape_and_cache
 
 if _is_xpu:
     from sgl_kernel import fused_qk_rope_with_cos_sin_cache_inplace
@@ -127,7 +125,7 @@ class RotaryEmbedding(MultiPlatformOp):
         self._apply_rotary_emb_wrapped = apply_rotary_emb
 
         # XXX (MUSA): Implement sgl_kernel.rotary_embedding support for MUSA backend
-        if get_server_args().rl_on_policy_target is not None or _is_musa:
+        if get_exec().deterministic.rl_on_policy_target is not None or _is_musa:
             self._forward_method = self.forward_native
             self._apply_rotary_emb_wrapped = torch.compile(
                 dynamic=True,
@@ -151,7 +149,7 @@ class RotaryEmbedding(MultiPlatformOp):
         # create the cache on GPU for faster initialization. This may cause
         # a slight numerical difference between the HF implementation and ours.
         init_device = (
-            "cpu" if get_server_args().rl_on_policy_target is not None else None
+            "cpu" if get_exec().deterministic.rl_on_policy_target is not None else None
         )
         inv_freq = 1.0 / (
             base
@@ -162,7 +160,7 @@ class RotaryEmbedding(MultiPlatformOp):
                 / self.rotary_dim
             )
         )
-        if get_server_args().rl_on_policy_target is not None:
+        if get_exec().deterministic.rl_on_policy_target is not None:
             inv_freq = inv_freq.cuda()
         return inv_freq
 

python/sglang/srt/layers/rotary_embedding/mrope.py

@@ -18,7 +18,7 @@ from sglang.srt.layers.rotary_embedding.yarn import (
     yarn_get_mscale_simple,
     yarn_linear_ramp_mask,
 )
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     cpu_has_amx_support,
     is_cuda,
@@ -42,7 +42,6 @@ if _is_xpu:
     from sgl_kernel import multimodal_rotary_embedding
 
 from sglang.kernels.ops.attention.mrope import apply_interleaved_rope_triton
-from sglang.srt.runtime_context import get_server_args
 
 
 def apply_interleaved_rope(x: torch.Tensor, mrope_section: list) -> torch.Tensor:
@@ -132,7 +131,7 @@ class MRotaryEmbedding(RotaryEmbedding):
             self.register_buffer("axis_map", axis_map, persistent=False)
         else:
             self.axis_map = None
-        if get_server_args().rl_on_policy_target is not None:
+        if get_exec().deterministic.rl_on_policy_target is not None:
             self._forward_method = self.forward_native
 
     def get_cos_sin_with_position(self, positions):
@@ -144,7 +143,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         last_dim = cos_sin.size()[-1]
         cos, sin = cos_sin.chunk(2, dim=-1)
         if self.mrope_interleaved:
-            if support_triton(get_server_args().attention_backend):
+            if support_triton(get_exec().kernel.attention_backend):
                 cos = apply_interleaved_rope_triton(cos, self.mrope_section)
                 sin = apply_interleaved_rope_triton(sin, self.mrope_section)
             else:

python/sglang/srt/layers/sampler.py

@@ -8,34 +8,21 @@ from torch import nn
 
 from sglang.kernels.ops.sampling.murmur_hash import murmur_hash32
 from sglang.srt.distributed import get_tp_group
-from sglang.srt.layers.dp_attention import (
-    is_dp_attention_enabled,
-)
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.layers.logprob_processor import (
-    OutputLogprobProcessor,
-)
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.layers.logprob_processor import OutputLogprobProcessor
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.sampling.sampling_params import TOP_K_ALL
 from sglang.srt.utils.async_probe import sanitize_nan_logits
-from sglang.srt.utils.common import (
-    get_bool_env_var,
-    is_cuda,
-    is_hip,
-    is_musa,
-    is_npu,
-)
+from sglang.srt.utils.common import get_bool_env_var, is_cuda, is_hip, is_musa, is_npu
 
 if is_cuda():
     from flashinfer.sampling import (
         min_p_sampling_from_probs,
         top_k_top_p_sampling_from_probs,
     )
-    from sgl_kernel import (
-        top_k_renorm_prob,
-        top_p_renorm_prob,
-    )
+    from sgl_kernel import top_k_renorm_prob, top_p_renorm_prob
 
 if is_musa():
     from sgl_kernel import (
@@ -74,12 +61,14 @@ class Sampler(nn.Module):
         if is_dp_attention_enabled():
             self.tp_sync_group = get_parallel().attn_tp_group.device_group
 
-        self.rl_on_policy_target = get_server_args().rl_on_policy_target
+        self.rl_on_policy_target = get_exec().deterministic.rl_on_policy_target
         # In RL on-policy mode, deterministic inference is automatically enabled.
-        self.enable_deterministic = get_server_args().enable_deterministic_inference
+        self.enable_deterministic = (
+            get_exec().deterministic.enable_deterministic_inference
+        )
         # In RL on-policy mode, we use log_softmax to compute logprobs to match the trainer.
         self.use_log_softmax_logprob = self.rl_on_policy_target is not None
-        self.use_ascend_backend = get_server_args().sampling_backend == "ascend"
+        self.use_ascend_backend = get_exec().kernel.sampling_backend == "ascend"
 
         self.output_logprob_processor = OutputLogprobProcessor()
 
@@ -245,7 +234,7 @@ class Sampler(nn.Module):
                 positions=positions,
             )
         else:
-            backend = get_server_args().sampling_backend
+            backend = get_exec().kernel.sampling_backend
             if backend == "flashinfer":
                 assert (
                     sampling_info.sampling_seed is None

python/sglang/srt/managers/data_parallel_controller.py

@@ -48,6 +48,7 @@ from sglang.srt.managers.scheduler import run_scheduler_process
 from sglang.srt.observability.cpu_monitor import start_cpu_monitor_thread
 from sglang.srt.observability.req_time_stats import DPControllerReqTimeStats
 from sglang.srt.observability.trace import process_tracing_init, trace_set_thread_info
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.server_args import (
     DP_ATTENTION_HANDSHAKE_PORT_DELTA,
     PortArgs,
@@ -231,7 +232,7 @@ class DataParallelController:
                 sock_send(worker, obj)
 
     def update_active_ranks(self, ranks: ActiveRanksOutput):
-        if self.server_args.elastic_ep_backend is not None:
+        if get_exec().moe.elastic_ep_backend is not None:
             if len(ranks.status) != self.max_dp_size:
                 logger.warning(
                     "[Elastic EP][DPC] active rank status len=%d != max_dp_size=%d; "
@@ -484,7 +485,7 @@ class DataParallelController:
             logger.debug("Worker port broadcast completed")
             return worker_ports
         finally:
-            if self.server_args.elastic_ep_backend is None:
+            if get_exec().moe.elastic_ep_backend is None:
                 rep_socket.close()
             else:
                 threading.Thread(

python/sglang/srt/managers/mm_utils.py

@@ -33,7 +33,13 @@ from sglang.srt.managers.schedule_batch import (
 from sglang.srt.mem_cache.multimodal_cache import EmbeddingResult, MultiModalStaticCache
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.multimodal.evs import EVSEmbeddingResult
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_disagg,
+    get_parallel,
+    get_schedule,
+    get_server_args,
+    get_serving,
+)
 from sglang.srt.utils import flatten_nested_list, is_hip, is_npu, print_warning_once
 from sglang.srt.utils.stale_shm_cleanup import make_shm_name
 from sglang.utils import logger
@@ -878,7 +884,7 @@ def _adjust_embedding_length(
             f"tokens from multimodal embeddings."
         )
         if num_mm_tokens_in_input_ids < num_mm_tokens_in_embedding:
-            chunked_prefill_size = get_server_args().chunked_prefill_size
+            chunked_prefill_size = get_schedule().chunked_prefill_size
             if chunked_prefill_size != -1:
                 logger.warning(
                     "You may want to avoid this issue by raising `chunked_prefill_size`, or disabling chunked prefill"
@@ -1287,7 +1293,7 @@ def general_mm_embed_routine(
                             feature = getattr(mm_item, "feature", None)
                             if isinstance(feature, torch.Tensor) and feature.is_cuda:
                                 mm_item.feature = feature.to("cpu", non_blocking=True)
-                            if get_server_args().language_only:
+                            if get_disagg().language_only:
                                 precomputed_embeddings = getattr(
                                     mm_item, "precomputed_embeddings", None
                                 )
@@ -1967,7 +1973,7 @@ def wrap_shm_features(obj):
     """
     Scan the object for multimodal tensors and wrap them in SHM pointers.
     """
-    if _get_is_default_transport() or get_server_args().skip_tokenizer_init:
+    if _get_is_default_transport() or get_serving().skip_tokenizer_init:
         return obj
 
     if obj.mm_inputs:
@@ -2028,7 +2034,7 @@ def unwrap_shm_features(obj):
     Restore ShmPointerMMData wrappers back into standard torch.Tensors.
     Handles both single requests and batch requests.
     """
-    if _get_is_default_transport() or get_server_args().skip_tokenizer_init:
+    if _get_is_default_transport() or get_serving().skip_tokenizer_init:
         return obj
     # Handle batch requests
     if isinstance(obj, BaseBatchReq):

python/sglang/srt/managers/multi_tokenizer_mixin.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from sglang.srt.runtime_context import get_disagg
+
 # 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.
@@ -645,15 +647,15 @@ class TokenizerWorker(TokenizerManager):
         self.tokenizer_ipc_name = port_args.tokenizer_ipc_name
 
         # For PD disaggregtion
-        self.server_args.override(
+        from sglang.srt.runtime_context import get_context
+
+        get_context().override(
             "tokenizer_worker.restore_disaggregation_mode",
             disaggregation_mode=disaggregation_mode,
         )
-        self.disaggregation_mode = DisaggregationMode(
-            self.server_args.disaggregation_mode
-        )
+        self.disaggregation_mode = DisaggregationMode(get_disagg().disaggregation_mode)
         self.disaggregation_transfer_backend = TransferBackend(
-            self.server_args.disaggregation_transfer_backend
+            get_disagg().disaggregation_transfer_backend
         )
 
         # Register this worker with the router for pause/continue broadcasting

python/sglang/srt/managers/schedule_batch.py

@@ -77,10 +77,7 @@ from sglang.srt.managers.embed_types import PositionalEmbeds
 from sglang.srt.managers.scheduler_components.new_token_ratio_tracker import (
     NewTokenRatioTracker,
 )
-from sglang.srt.mem_cache.allocation import (
-    alloc_for_decode,
-    alloc_for_extend,
-)
+from sglang.srt.mem_cache.allocation import alloc_for_decode, alloc_for_extend
 from sglang.srt.mem_cache.allocation_sizing import get_alloc_reserve_per_decode
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import (
@@ -105,7 +102,12 @@ from sglang.srt.observability.req_time_stats import (
     DPControllerReqTimeStats,
     SchedulerReqTimeStats,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_parallel,
+    get_server_args,
+    get_serving,
+    get_spec,
+)
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.server_args import ServerArgs
@@ -1094,7 +1096,7 @@ class Req(ReqDllmMixin):
         """Check if this request is prefill-only (no token generation needed)."""
         # NOTE: when spec is enabled, prefill_only optimizations are disabled
 
-        spec_alg = get_server_args().speculative_algorithm
+        spec_alg = get_spec().speculative_algorithm
         return self.sampling_params.max_new_tokens == 0 and spec_alg is None
 
     @property
@@ -1115,7 +1117,7 @@ class Req(ReqDllmMixin):
     def effective_kv_committed_len(self) -> int:
         # Report only the prompt prefix so thinking + answer fall into the
         # overallocated range and are reclaimed by release_kv_cache. #22373.
-        if get_server_args().strip_thinking_cache and self.reasoning_tokens > 0:
+        if get_serving().strip_thinking_cache and self.reasoning_tokens > 0:
             return min(self.kv_committed_len, len(self.origin_input_ids))
         return self.kv_committed_len
 

python/sglang/srt/managers/schedule_policy.py

@@ -56,7 +56,7 @@ from sglang.srt.mem_cache.multi_ended_allocator import (
     UnifiedMambaTokenToKVPoolAllocator,
 )
 from sglang.srt.mem_cache.radix_cache import RadixCache, RadixKey, TreeNode
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_disagg
 from sglang.srt.server_args import ServerArgs
 
 if TYPE_CHECKING:
@@ -193,7 +193,7 @@ class SchedulePolicy:
         if (
             not isinstance(policy, CacheAwarePolicy)
             and self.tree_cache.supports_fast_match_prefix()
-            and get_server_args().disaggregation_mode != "decode"
+            and get_disagg().disaggregation_mode != "decode"
         ):
             for r in waiting_queue:
                 match_prefix_for_req(self.tree_cache, r, include_req=True)

python/sglang/srt/managers/scheduler.py

@@ -210,9 +210,7 @@ from sglang.srt.managers.scheduler_components.pool_stats_observer import (
 from sglang.srt.managers.scheduler_components.profiler_manager import (
     SchedulerProfilerManager,
 )
-from sglang.srt.managers.scheduler_components.recv_skipper import (
-    SchedulerRecvSkipper,
-)
+from sglang.srt.managers.scheduler_components.recv_skipper import SchedulerRecvSkipper
 from sglang.srt.managers.scheduler_components.request_receiver import (
     SchedulerRequestReceiver,
 )
@@ -241,7 +239,20 @@ from sglang.srt.observability.trace import process_tracing_init, trace_set_threa
 from sglang.srt.parser.reasoning_parser import ReasoningParser
 from sglang.srt.platforms import current_platform
 from sglang.srt.plugins import load_plugins
-from sglang.srt.runtime_context import get_context, get_parallel
+from sglang.srt.runtime_context import (
+    get_context,
+    get_device,
+    get_disagg,
+    get_exec,
+    get_lora,
+    get_memory,
+    get_mm,
+    get_observability,
+    get_parallel,
+    get_schedule,
+    get_serving,
+    get_spec,
+)
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.sampling.sampling_params import TOP_K_ALL
 from sglang.srt.server_args import PortArgs, ServerArgs
@@ -443,9 +454,9 @@ class Scheduler(
             attn_tp_cpu_group=self.attn_tp_cpu_group,
             tp_cpu_group=self.tp_cpu_group,
             attn_cp_cpu_group=self.attn_cp_cpu_group,
-            enable_metrics=self.server_args.enable_metrics,
+            enable_metrics=get_observability().enable_metrics,
             enable_kv_cache_events=bool(
-                self.server_args.kv_events_config
+                get_observability().kv_events_config
                 and self.ps.pp_rank == 0
                 and self.ps.attn_tp_rank == 0
                 and self.ps.attn_cp_rank == 0
@@ -471,8 +482,8 @@ class Scheduler(
         self.init_hisparse_coordinator()
 
         if (
-            self.server_args.disaggregation_mode == "decode"
-            and self.server_args.disaggregation_decode_enable_offload_kvcache
+            get_disagg().disaggregation_mode == "decode"
+            and get_disagg().disaggregation_decode_enable_offload_kvcache
         ):
             self.decode_offload_manager = DecodeKVCacheOffloadManager(
                 req_to_token_pool=self.req_to_token_pool,
@@ -583,7 +594,7 @@ class Scheduler(
 
             self.dllm_config = (  # For diffusion LLM
                 DllmConfig.from_server_args(self.server_args)
-                if self.server_args.dllm_algorithm is not None
+                if get_exec().dllm.dllm_algorithm is not None
                 else None
             )
 
@@ -611,11 +622,11 @@ class Scheduler(
         self.ipc_channels = SchedulerIpcChannels.create(
             port_args=port_args,
             is_rank_zero=is_rank_zero,
-            skip_tokenizer_init=self.server_args.skip_tokenizer_init,
-            metrics_enabled=self.server_args.enable_metrics
+            skip_tokenizer_init=get_serving().skip_tokenizer_init,
+            metrics_enabled=get_observability().enable_metrics
             and (
                 self.ps.attn_tp_rank == 0
-                or self.server_args.enable_metrics_for_all_schedulers
+                or get_observability().enable_metrics_for_all_schedulers
             ),
             enable_scripted_runtime=envs.SGLANG_TEST_SCRIPTED_RUNTIME.get(),
         )
@@ -631,7 +642,7 @@ class Scheduler(
                 port_args,
                 self.ps.dp_size,
                 dp_rank,
-                publish_interval=self.server_args.load_snapshot_publish_interval,
+                publish_interval=get_observability().load_snapshot_publish_interval,
             )
         except Exception as e:
             logger.warning("load snapshot writer init failed: %s", e)
@@ -641,7 +652,7 @@ class Scheduler(
             self.ps.pp_rank == 0
             and self.ps.attn_tp_rank == 0
             and self.ps.attn_cp_rank == 0
-            and self.server_args.sleep_on_idle
+            and get_device().sleep_on_idle
         ):
             self.idle_sleeper = IdleSleeper(
                 sockets=[
@@ -712,9 +723,9 @@ class Scheduler(
                 )
 
         # Set reasoning_parser and think_end_id if --reasoning_parser is enabled
-        if self.server_args.reasoning_parser and self.tokenizer:
+        if get_serving().reasoning_parser and self.tokenizer:
             reasoning_parser = ReasoningParser(
-                model_type=self.server_args.reasoning_parser,
+                model_type=get_serving().reasoning_parser,
                 stream_reasoning=False,
                 tokenizer=self.tokenizer,
             )
@@ -785,7 +796,7 @@ class Scheduler(
             target_worker=self.tp_worker,
         )
 
-        if self.server_args.speculative_draft_load_format is not None:
+        if get_spec().speculative_draft_load_format is not None:
             # Write the draft load_format onto server_args (not just the bag):
             # the draft worker is built from a copy of self.server_args and
             # build_load_config reads server_args.load_format, so a bag-only
@@ -793,10 +804,10 @@ class Scheduler(
             # format.
             self.server_args.override(
                 "scheduler.draft_load_format",
-                load_format=self.server_args.speculative_draft_load_format,
+                load_format=get_spec().speculative_draft_load_format,
             )
             logger.info(
-                f"Using draft model load_format: '{self.server_args.speculative_draft_load_format}'"
+                f"Using draft model load_format: '{get_spec().speculative_draft_load_format}'"
             )
 
         DraftWorkerClass = self.spec_algorithm.create_worker(self.server_args)
@@ -887,7 +898,7 @@ class Scheduler(
         # --min-free-slots-delay. Built independently of the prefill delayer.
         self.min_free_slots_delayer: Optional[MinFreeSlotsDelayer] = None
         min_free_slots = resolve_min_free_slots(
-            self.server_args.min_free_slots_delay,
+            get_schedule().min_free_slots_delay,
             self.max_running_requests,
             is_dflash_family=self.spec_algorithm.is_dflash_family(),
         )
@@ -933,14 +944,14 @@ class Scheduler(
         if self.ps.tp_rank == 0:
             logger.info(
                 f"max_total_num_tokens={self.max_total_num_tokens}, "
-                f"chunked_prefill_size={self.server_args.chunked_prefill_size}, "
+                f"chunked_prefill_size={get_schedule().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.server_args.enable_metrics:
+        if get_observability().enable_metrics:
             self.metrics_collector.emit_constants(
                 max_total_num_tokens=self.max_total_num_tokens,
                 # TODO: max_running_requests_under_SLO has no setter — dead chain.
@@ -987,7 +998,7 @@ class Scheduler(
         self._engine_paused = False
 
     def init_chunked_prefill(self):
-        self.chunked_prefill_size = self.server_args.chunked_prefill_size
+        self.chunked_prefill_size = get_schedule().chunked_prefill_size
         uses_transformers_backend = (
             get_resolved_model_impl(self.model_config) == ModelImpl.TRANSFORMERS
         )
@@ -1007,13 +1018,12 @@ class Scheduler(
         self.chunked_req = None
         self._pending_chunked_abort_req = None
         self.is_mixed_chunk = (
-            self.chunked_prefill_size is not None
-            and self.server_args.enable_mixed_chunk
+            self.chunked_prefill_size is not None and get_schedule().enable_mixed_chunk
         )
 
         # Init the dynamic chunking predictor for PP
         self.enable_dynamic_chunking = (
-            self.server_args.enable_dynamic_chunking and self.ps.pp_size > 1
+            get_schedule().enable_dynamic_chunking and self.ps.pp_size > 1
         )
         if self.enable_dynamic_chunking:
             try:
@@ -1049,8 +1059,8 @@ class Scheduler(
         )
         self.prefill_delayer: Optional[PrefillDelayer] = None
         self.max_prefill_bs: int = 0
-        if self.server_args.enable_prefill_delayer:
-            if self.server_args.disaggregation_mode == "decode":
+        if get_schedule().enable_prefill_delayer:
+            if get_disagg().disaggregation_mode == "decode":
                 logger.info(
                     "Ignoring --enable-prefill-delayer on decode engine "
                     "(no prefill scheduling path; delayer would be a no-op)."
@@ -1067,15 +1077,15 @@ class Scheduler(
                         if self.metrics_reporter.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,
+                    max_delay_passes=get_schedule().prefill_delayer_max_delay_passes,
+                    token_usage_low_watermark=get_schedule().prefill_delayer_token_usage_low_watermark,
                     device=self.tp_group.device,
                 )
 
         # 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
+            and not get_schedule().disable_priority_preemption
         )
 
         self.new_token_ratio_tracker = NewTokenRatioTracker.from_server_args(
@@ -1091,12 +1101,12 @@ class Scheduler(
     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
+            self, watchdog_timeout=get_device().watchdog_timeout
         )
 
         # Init memory saver, profiler and metric stats
         self.memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=self.server_args.enable_memory_saver
+            enable=get_exec().features.enable_memory_saver
         )
 
         # Init recv skipper and input blocker
@@ -1118,11 +1128,9 @@ class Scheduler(
         self.disagg_decode_prealloc_queue = None
         self.disagg_decode_transfer_queue = None
 
-        self.disaggregation_mode = DisaggregationMode(
-            self.server_args.disaggregation_mode
-        )
+        self.disaggregation_mode = DisaggregationMode(get_disagg().disaggregation_mode)
         self.transfer_backend = TransferBackend(
-            self.server_args.disaggregation_transfer_backend
+            get_disagg().disaggregation_transfer_backend
         )
 
         # 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?
@@ -1192,10 +1200,10 @@ class Scheduler(
                 tp_size=self.ps.tp_size,
                 dp_size=self.server_args.dp_size,
                 gpu_id=self.ps.gpu_id,
-                bootstrap_port=self.server_args.disaggregation_bootstrap_port,
+                bootstrap_port=get_disagg().disaggregation_bootstrap_port,
                 max_total_num_tokens=self.max_total_num_tokens,
                 pp_rank=self.ps.pp_rank,
-                num_reserved_decode_tokens=self.server_args.num_reserved_decode_tokens,
+                num_reserved_decode_tokens=get_disagg().num_reserved_decode_tokens,
                 transfer_backend=self.transfer_backend,
             )
 
@@ -1221,7 +1229,7 @@ class Scheduler(
                 tp_rank=self.ps.tp_rank,
                 tp_size=self.ps.tp_size,
                 gpu_id=self.ps.gpu_id,
-                bootstrap_port=self.server_args.disaggregation_bootstrap_port,
+                bootstrap_port=get_disagg().disaggregation_bootstrap_port,
                 gloo_group=self.attn_tp_cpu_group,
                 max_total_num_tokens=self.max_total_num_tokens,
                 scheduler=self,
@@ -1235,11 +1243,10 @@ class Scheduler(
             self.enable_staging = envs.SGLANG_DISAGG_STAGING_BUFFER.get()
 
         # Init mm receiver for EPD disaggregation mode
-        if (
-            self.server_args.language_only
-            and self.server_args.encoder_transfer_backend
-            in ["zmq_to_scheduler", "mooncake"]
-        ):
+        if get_disagg().language_only and get_disagg().encoder_transfer_backend in [
+            "zmq_to_scheduler",
+            "mooncake",
+        ]:
             self.mm_receiver = create_mm_receiver(
                 self.server_args,
                 dtype=self.model_config.dtype,
@@ -1320,7 +1327,7 @@ class Scheduler(
 
     def init_deterministic_inference_config(self):
         """Initialize deterministic inference configuration for different attention backends."""
-        if not self.server_args.enable_deterministic_inference:
+        if not get_exec().deterministic.enable_deterministic_inference:
             self.truncation_align_size = None
             return
 
@@ -1329,7 +1336,7 @@ class Scheduler(
             "triton": ("SGLANG_TRITON_PREFILL_TRUNCATION_ALIGN_SIZE", 4096),
         }
         env_var, default_size = backend_sizes.get(
-            self.server_args.attention_backend, (None, None)
+            get_exec().kernel.attention_backend, (None, None)
         )
         self.truncation_align_size = (
             get_int_env_var(env_var, default_size) if env_var else None
@@ -1725,10 +1732,10 @@ class Scheduler(
         )
 
     def init_lora_drainer(self) -> None:
-        if self.server_args.lora_drain_wait_threshold > 0.0:
+        if get_lora().lora_drain_wait_threshold > 0.0:
             self.lora_drainer = LoRADrainer(
-                self.server_args.max_loras_per_batch,
-                self.server_args.lora_drain_wait_threshold,
+                get_lora().max_loras_per_batch,
+                get_lora().lora_drain_wait_threshold,
             )
         else:
             self.lora_drainer = None
@@ -1830,7 +1837,7 @@ class Scheduler(
 
     def init_kv_events_publisher(self) -> None:
         self.kv_events_publisher = SchedulerKvEventsPublisher(
-            kv_events_config=self.server_args.kv_events_config,
+            kv_events_config=get_observability().kv_events_config,
             ps=self.ps,
             attn_tp_rank=self.ps.attn_tp_rank,
             attn_cp_rank=self.ps.attn_cp_rank,
@@ -2006,7 +2013,7 @@ class Scheduler(
         return image_inputs
 
     def _get_multimodal_inputs(self, mm_inputs_dict):
-        if self.server_args.enable_broadcast_mm_inputs_process:
+        if get_mm().enable_broadcast_mm_inputs_process:
             return self._process_and_broadcast_mm_inputs(mm_inputs_dict)
         else:
             return MultimodalInputs.from_processor_output(mm_inputs_dict)
@@ -2053,7 +2060,7 @@ class Scheduler(
 
     def _maybe_namespace_elastic_radix_cache(self, req: Req) -> None:
         if (
-            self.server_args.elastic_ep_backend is None
+            get_exec().moe.elastic_ep_backend is None
             or self.disable_radix_cache
             or not self.tree_cache.is_tree_cache()
         ):
@@ -2099,8 +2106,7 @@ class Scheduler(
         )
         # Radix-native sessions use only the top-level session_id.
         radix_native_session = (
-            recv_req.session_id is not None
-            and self.server_args.enable_session_radix_cache
+            recv_req.session_id is not None and get_memory().enable_session_radix_cache
         )
 
         if session_id is None or radix_native_session:
@@ -2112,7 +2118,7 @@ class Scheduler(
 
             if recv_req.bootstrap_port is None:
                 # Use default bootstrap port
-                recv_req.bootstrap_port = self.server_args.disaggregation_bootstrap_port
+                recv_req.bootstrap_port = get_disagg().disaggregation_bootstrap_port
 
             req = Req(
                 recv_req.rid,
@@ -2265,7 +2271,7 @@ class Scheduler(
             self._add_request_to_queue(req)
             return
 
-        if req.return_sampling_mask and self.server_args.sampling_backend == "ascend":
+        if req.return_sampling_mask and get_exec().kernel.sampling_backend == "ascend":
             # The ascend backend samples from logits directly and never builds the
             # top-k/top-p support, so it cannot produce a sampling mask.
             error_msg = (
@@ -2314,7 +2320,7 @@ class Scheduler(
         error_msg = validate_input_length(
             req,
             self.max_req_input_len,
-            self.server_args.allow_auto_truncate,
+            get_serving().allow_auto_truncate,
         )
         if error_msg:
             req.set_finish_with_abort(error_msg)
@@ -2592,7 +2598,7 @@ class Scheduler(
         error_msg = validate_input_length(
             req,
             self.max_req_input_len,
-            self.server_args.allow_auto_truncate,
+            get_serving().allow_auto_truncate,
         )
         if error_msg:
             self._add_request_to_queue(req)
@@ -2804,7 +2810,7 @@ class Scheduler(
         if (
             need_mlp_sync
             and not self.spec_algorithm.is_none()
-            and not self.server_args.speculative_skip_dp_mlp_sync
+            and not get_spec().speculative_skip_dp_mlp_sync
         ):
             # 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:
@@ -2878,7 +2884,7 @@ class Scheduler(
             for req in ready_grammar_requests:
                 self._add_request_to_queue(req)
 
-        if self.enable_hierarchical_cache or self.server_args.enable_flexkv:
+        if self.enable_hierarchical_cache or get_memory().enable_flexkv:
             self.tree_cache.check_hicache_events()
 
         if self.enable_priority_preemption or self.is_hybrid_swa:
@@ -2945,7 +2951,7 @@ class Scheduler(
             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_max_requests=get_schedule().prefill_max_requests,
             prefill_delayer_single_pass=prefill_delayer_single_pass,
             dllm_config=self.dllm_config,
             waiting_queue_len=len(self.waiting_queue),
@@ -3516,7 +3522,7 @@ class Scheduler(
 
     def _maybe_report_active_ranks(self) -> None:
         if not (
-            self.enable_dp_attention and self.server_args.elastic_ep_backend is not None
+            self.enable_dp_attention and get_exec().moe.elastic_ep_backend is not None
         ):
             return
         from sglang.srt.elastic_ep.elastic_ep import ElasticEPStateManager
@@ -3792,7 +3798,7 @@ class Scheduler(
             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,
+                served_model_name=get_serving().served_model_name,
                 hicache_storage_prefetch_policy=recv_req.hicache_storage_prefetch_policy,
                 hicache_write_policy=recv_req.hicache_write_policy,
             )
@@ -3912,7 +3918,7 @@ class Scheduler(
         }
         ret["effective_max_running_requests_per_dp"] = self.max_running_requests
 
-        if self.server_args.elastic_ep_backend is not None:
+        if get_exec().moe.elastic_ep_backend is not None:
             from sglang.srt.elastic_ep.elastic_ep import ElasticEPStateManager
 
             ret["is_scaling_elastic_ep"] = ElasticEPStateManager.is_scaling()
@@ -4445,10 +4451,10 @@ class Scheduler(
         return None
 
     def close_session(self, recv_req: CloseSessionReqInput):
-        if self.server_args.enable_session_radix_cache:
+        if get_memory().enable_session_radix_cache:
             self.tree_cache.release_radix_session(recv_req.session_id)
         if recv_req.session_id in self.session_controller or not (
-            self.server_args.enable_session_radix_cache
+            get_memory().enable_session_radix_cache
         ):
             self.session_controller.close(recv_req)
 

python/sglang/srt/managers/scheduler_components/batch_result_processor.py

@@ -2,14 +2,7 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from typing import (
-    TYPE_CHECKING,
-    Callable,
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
+from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
 
 import torch
 
@@ -23,11 +16,14 @@ from sglang.srt.managers.schedule_batch import (
     ScheduleBatch,
     mamba_lazy_spec_in_window,
 )
-from sglang.srt.mem_cache.common import (
-    maybe_cache_unfinished_req,
-    release_kv_cache,
+from sglang.srt.mem_cache.common import maybe_cache_unfinished_req, release_kv_cache
+from sglang.srt.runtime_context import (
+    get_disagg,
+    get_exec,
+    get_memory,
+    get_observability,
+    get_server_args,
 )
-from sglang.srt.runtime_context import get_server_args
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker
 from sglang.srt.state_capturer.indexer_topk import get_global_indexer_capturer
 from sglang.srt.state_capturer.routed_experts import get_global_experts_capturer
@@ -48,10 +44,7 @@ if TYPE_CHECKING:
         SchedulerOutputStreamer,
     )
     from sglang.srt.managers.tp_worker import BaseTpWorker
-    from sglang.srt.managers.utils import (
-        EmbeddingBatchResult,
-        GenerationBatchResult,
-    )
+    from sglang.srt.managers.utils import EmbeddingBatchResult, GenerationBatchResult
     from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
     from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
     from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
@@ -84,7 +77,7 @@ class SchedulerBatchResultProcessor:
 
     def process_batch_result_prebuilt(self, batch: ScheduleBatch):
         assert self.disaggregation_mode == DisaggregationMode.DECODE
-        use_free_group = self.server_args.disaggregation_decode_enable_radix_cache
+        use_free_group = get_disagg().disaggregation_decode_enable_radix_cache
         if use_free_group:
             self.token_to_kv_pool_allocator.free_group_begin()
         for req in batch.reqs:
@@ -92,7 +85,7 @@ class SchedulerBatchResultProcessor:
             req.update_finish_state()
             if req.finished():
                 req.time_stats.set_quick_finish_time()
-                if self.server_args.enable_hisparse:
+                if get_memory().enable_hisparse:
                     self.hisparse_coordinator.request_finished(req)
                 release_kv_cache(req, self.tree_cache)
 
@@ -243,7 +236,7 @@ class SchedulerBatchResultProcessor:
                         req.time_stats.set_completion_time()
                     elif not batch.decoding_reqs or req not in batch.decoding_reqs:
                         maybe_cache_unfinished_req(req, self.tree_cache)
-                        if self.server_args.enable_hisparse:
+                        if get_memory().enable_hisparse:
                             self.hisparse_coordinator.admit_request_into_staging(req)
 
                     self._maybe_collect_customized_info(i, req, logits_output)
@@ -756,7 +749,7 @@ class SchedulerBatchResultProcessor:
                 num_block_accept_tokens=result.num_block_accept_tokens,
                 num_cap_tokens=result.num_cap_tokens,
             )
-        if self.server_args.enable_metrics:
+        if get_observability().enable_metrics:
             self.metrics_collector.increment_decode_cuda_graph_pass(
                 value=can_run_cuda_graph
             )
@@ -939,7 +932,7 @@ class SchedulerBatchResultProcessor:
         self._mamba_prefix_cache_update(req, batch, result, i)
 
         if (
-            self.server_args.disaggregation_decode_enable_offload_kvcache
+            get_disagg().disaggregation_decode_enable_offload_kvcache
             and not req.finished()
         ):
             self.decode_offload_manager.offload_kv_cache(req)
@@ -959,12 +952,12 @@ class SchedulerBatchResultProcessor:
             self._maybe_collect_routed_experts(req)
             self._maybe_collect_indexer_topk(req)
 
-            if self.server_args.disaggregation_decode_enable_offload_kvcache:
+            if get_disagg().disaggregation_decode_enable_offload_kvcache:
                 # Asynchronously offload KV cache; release_kv_cache will be called after Device->Host transfer completes
                 if not self.decode_offload_manager.offload_kv_cache(req):
                     self.decode_offload_manager.finalize_release_on_finish(req)
             else:
-                if self.server_args.enable_hisparse:
+                if get_memory().enable_hisparse:
                     self.hisparse_coordinator.request_finished(req)
                 prepare_release = getattr(
                     self.model_worker, "prepare_for_kv_cache_release", None
@@ -1102,7 +1095,7 @@ class SchedulerBatchResultProcessor:
         For spec decode, the boundary is detected by comparing the
         accepted seq_len range against interval boundaries.
         """
-        interval = get_server_args().mamba_track_interval
+        interval = get_exec().mamba.mamba_track_interval
 
         if batch.spec_algorithm.is_none():
             if req.kv_committed_len % interval == 0:

python/sglang/srt/managers/scheduler_components/dp_attn.py

@@ -12,9 +12,7 @@ from sglang.srt.distributed.parallel_state_wrapper import ParallelState
 from sglang.srt.environ import envs
 from sglang.srt.layers.dp_attention import world_dp_gather_enabled
 from sglang.srt.managers.schedule_batch import ScheduleBatch
-from sglang.srt.managers.scheduler_components.recv_skipper import (
-    SchedulerRecvSkipper,
-)
+from sglang.srt.managers.scheduler_components.recv_skipper import SchedulerRecvSkipper
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
@@ -26,6 +24,7 @@ from sglang.srt.model_executor.cuda_graph_config import (
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.observability.metrics_collector import DPCooperationInfo
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils.common import require_mlp_tp_gather
@@ -385,7 +384,7 @@ class SchedulerDPAttnAdapter:
             get_idle_batch=self.get_idle_batch,
             disable_cuda_graph=cuda_graph_fully_disabled(),
             require_mlp_tp_gather=require_mlp_tp_gather(self.server_args),
-            disable_overlap_schedule=self.server_args.disable_overlap_schedule,
+            disable_overlap_schedule=get_schedule().disable_overlap_schedule,
             offload_tags=self.offload_tags,
             dwdp=self.server_args.dwdp_size > 1,
         )

python/sglang/srt/managers/scheduler_components/load_inquirer.py

@@ -14,6 +14,7 @@ from sglang.srt.managers.load_snapshot import (
     QueueMetrics,
     SpeculativeMetrics,
 )
+from sglang.srt.runtime_context import get_lora
 
 if TYPE_CHECKING:
     from sglang.srt.distributed.parallel_state_wrapper import ParallelState
@@ -144,7 +145,7 @@ class SchedulerLoadInquirer:
             )
 
         lora = None
-        if self.server_args.enable_lora:
+        if get_lora().enable_lora:
             lora = LoRAMetrics(
                 slots_used=stats.lora_pool_slots_used,
                 slots_total=stats.lora_pool_slots_total,

python/sglang/srt/managers/scheduler_components/logprob_result_processor.py

@@ -1,20 +1,15 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import (
-    List,
-    Tuple,
-)
+from typing import List, Tuple
 
 import torch
 
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.schedule_batch import Req
-from sglang.srt.server_args import (
-    MIS_DELIMITER_TOKEN_ID,
-    ServerArgs,
-)
+from sglang.srt.runtime_context import get_exec
+from sglang.srt.server_args import MIS_DELIMITER_TOKEN_ID, ServerArgs
 
 
 @dataclass(kw_only=True, slots=True, frozen=True)
@@ -164,7 +159,7 @@ class SchedulerLogprobResultProcessor:
         delimiter token receive logprobs.
         """
         return (
-            self.server_args.enable_mis
+            get_exec().features.enable_mis
             and req.is_prefill_only
             and req.multi_item_delimiter_indices is not None
         )

python/sglang/srt/managers/scheduler_components/output_streamer.py

@@ -2,12 +2,7 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass, field
-from typing import (
-    Any,
-    Callable,
-    List,
-    Optional,
-)
+from typing import Any, Callable, List, Optional
 
 import torch
 import zmq
@@ -21,11 +16,9 @@ from sglang.srt.managers.io_struct import (
     CachedTokensDetails,
     wrap_as_pickle,
 )
-from sglang.srt.managers.schedule_batch import (
-    BaseFinishReason,
-    Req,
-)
+from sglang.srt.managers.schedule_batch import BaseFinishReason, Req
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
+from sglang.srt.runtime_context import get_observability, get_serving
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 
@@ -144,7 +137,7 @@ class SchedulerOutputStreamer:
             return_sampling_mask=return_sampling_mask,
             spec_algorithm=self.spec_algorithm,
             disaggregation_mode=self.disaggregation_mode,
-            default_stream_interval=self.server_args.stream_interval,
+            default_stream_interval=get_serving().stream_interval,
             default_force_stream_interval=DEFAULT_FORCE_STREAM_INTERVAL,
             get_cached_tokens_details=self.get_cached_tokens_details,
         )
@@ -171,7 +164,7 @@ class SchedulerOutputStreamer:
         if (
             req.finished()
             and self.ps.attn_tp_rank == 0
-            and self.server_args.enable_request_time_stats_logging
+            and get_observability().enable_request_time_stats_logging
         ):
             req.log_time_stats()
 

python/sglang/srt/managers/scheduler_components/profiler_manager.py

@@ -5,13 +5,7 @@ import os
 import time
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    List,
-    Optional,
-)
+from typing import TYPE_CHECKING, Any, Callable, List, Optional
 
 import torch
 
@@ -19,7 +13,7 @@ from sglang.srt.environ import envs
 from sglang.srt.managers.io_struct import ProfileReq, ProfileReqOutput, ProfileReqType
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.platforms import current_platform
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_device
 from sglang.srt.utils import is_mps, is_npu
 from sglang.srt.utils.profile_merger import ProfileMerger
 from sglang.srt.utils.profile_utils import ProfileManager
@@ -255,7 +249,7 @@ class SchedulerProfilerManager:
             self.profile_in_progress = True
 
         if "CUDA_PROFILER" in activities:
-            if self.ps.gpu_id == get_server_args().base_gpu_id:
+            if self.ps.gpu_id == get_device().base_gpu_id:
                 torch.cuda.cudart().cudaProfilerStart()
             self.profile_in_progress = True
 
@@ -365,7 +359,7 @@ class SchedulerProfilerManager:
             torch.cuda.memory._record_memory_history(enabled=None)
 
         if "CUDA_PROFILER" in self.profiler_activities:
-            if self.ps.gpu_id == get_server_args().base_gpu_id:
+            if self.ps.gpu_id == get_device().base_gpu_id:
                 torch.cuda.cudart().cudaProfilerStop()
 
         merge_message = self._merge_profile_traces()

python/sglang/srt/managers/scheduler_components/request_receiver.py

@@ -2,14 +2,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 from http import HTTPStatus
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    List,
-    Optional,
-    Union,
-)
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union
 
 import zmq
 from torch.distributed import barrier
@@ -22,14 +15,9 @@ from sglang.srt.managers.io_struct import (
     TokenizedGenerateReqInput,
     sock_recv,
 )
-from sglang.srt.managers.mm_utils import (
-    has_shm_features,
-    unwrap_shm_features,
-)
-from sglang.srt.utils import (
-    broadcast_pyobj,
-    point_to_point_pyobj,
-)
+from sglang.srt.managers.mm_utils import has_shm_features, unwrap_shm_features
+from sglang.srt.runtime_context import get_disagg
+from sglang.srt.utils import broadcast_pyobj, point_to_point_pyobj
 from sglang.srt.utils.nvtx_utils import scheduler_nvtx_method
 
 if TYPE_CHECKING:
@@ -220,8 +208,8 @@ class SchedulerRequestReceiver:
         # Process MM requests under EPD-disaggregation mode
         if (
             self.ps.pp_rank == 0
-            and self.server_args.language_only
-            and self.server_args.encoder_transfer_backend
+            and get_disagg().language_only
+            and get_disagg().encoder_transfer_backend
             in ["zmq_to_scheduler", "mooncake"]
         ):
             recv_reqs, abort_reqs = self.mm_receiver.process_waiting_requests(recv_reqs)

python/sglang/srt/managers/scheduler_pp_mixin.py

@@ -36,6 +36,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     PPProxyTensors,
 )
 from sglang.srt.observability.req_time_stats import set_time_batch
+from sglang.srt.runtime_context import get_disagg
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.utils import DynamicGradMode, broadcast_pyobj, point_to_point_pyobj
 from sglang.srt.utils.common import get_device_module, is_xpu
@@ -479,7 +480,7 @@ class SchedulerPPMixin:
                     )
                 )
 
-                if self.server_args.disaggregation_decode_enable_offload_kvcache:
+                if get_disagg().disaggregation_decode_enable_offload_kvcache:
                     self.decode_offload_manager.check_offload_progress()
 
                 if rmbs[next_mb_id] is not None:
@@ -549,7 +550,7 @@ class SchedulerPPMixin:
                 + len(self.disagg_decode_transfer_queue.queue)
                 + len(self.disagg_decode_prealloc_queue.queue)
             )
-            if self.server_args.disaggregation_decode_enable_offload_kvcache:
+            if get_disagg().disaggregation_decode_enable_offload_kvcache:
                 queue_size += len(self.decode_offload_manager.ongoing_offload)
 
             if server_is_idle and queue_size == 0:

python/sglang/srt/managers/tokenizer_control_mixin.py

@@ -74,6 +74,7 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightsFromTensorReqOutput,
 )
 from sglang.srt.managers.load_snapshot import LoadSnapshot
+from sglang.srt.runtime_context import get_lora
 from sglang.srt.server_args import LoRARef, ServerArgs
 from sglang.srt.utils import (
     get_bool_env_var,
@@ -569,7 +570,7 @@ class TokenizerControlMixin:
         self.auto_create_handle_loop()
 
         try:
-            if not self.server_args.enable_lora:
+            if not get_lora().enable_lora:
                 raise ValueError(
                     "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
                 )
@@ -602,10 +603,10 @@ class TokenizerControlMixin:
                     await self.lora_registry.register(new_adapter)
                     self.lora_ref_cache[obj.lora_name] = new_adapter
 
-                if self.server_args.max_loaded_loras is not None:
+                if get_lora().max_loaded_loras is not None:
                     while (
                         self.lora_registry.num_registered_loras
-                        > self.server_args.max_loaded_loras
+                        > get_lora().max_loaded_loras
                     ):
                         lru_lora_name = await self.lora_registry.lru_lora_name(
                             exclude_pinned=True
@@ -619,7 +620,7 @@ class TokenizerControlMixin:
                         logger.info(
                             f"Unloading least recently used LoRA adapter '{lru_lora_name}' "
                             f"(current number of adapters: {self.lora_registry.num_registered_loras}, "
-                            f"max allowed: {self.server_args.max_loaded_loras})"
+                            f"max allowed: {get_lora().max_loaded_loras})"
                         )
 
                         unload_result = await self._unload_lora_adapter_locked(
@@ -647,7 +648,7 @@ class TokenizerControlMixin:
         self.auto_create_handle_loop()
 
         try:
-            if not self.server_args.enable_lora:
+            if not get_lora().enable_lora:
                 raise ValueError(
                     "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
                 )
@@ -672,10 +673,10 @@ class TokenizerControlMixin:
                 if result.success:
                     await self.lora_registry.register(new_adapter)
                     self.lora_ref_cache[obj.lora_name] = new_adapter
-                if self.server_args.max_loaded_loras is not None:
+                if get_lora().max_loaded_loras is not None:
                     while (
                         self.lora_registry.num_registered_loras
-                        > self.server_args.max_loaded_loras
+                        > get_lora().max_loaded_loras
                     ):
                         lru_lora_name = await self.lora_registry.lru_lora_name(
                             exclude_pinned=True
@@ -689,7 +690,7 @@ class TokenizerControlMixin:
                         logger.info(
                             f"Unloading least recently used LoRA adapter '{lru_lora_name}' "
                             f"(current number of adapters: {self.lora_registry.num_registered_loras}, "
-                            f"max allowed: {self.server_args.max_loaded_loras})"
+                            f"max allowed: {get_lora().max_loaded_loras})"
                         )
 
                         unload_result = await self._unload_lora_adapter_locked(
@@ -717,7 +718,7 @@ class TokenizerControlMixin:
         self.auto_create_handle_loop()
 
         try:
-            if not self.server_args.enable_lora:
+            if not get_lora().enable_lora:
                 raise ValueError(
                     "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
                 )
@@ -893,6 +894,8 @@ class TokenizerControlMixin:
     ) -> None:
         """Update weight version if provided."""
         if weight_version is not None:
-            self.server_args.override(
+            from sglang.srt.runtime_context import get_context
+
+            get_context().override(
                 "tokenizer.weight_version", weight_version=weight_version
             )

python/sglang/srt/managers/tokenizer_manager.py

@@ -110,6 +110,14 @@ from sglang.srt.observability.request_metrics_exporter import (
     RequestMetricsExporterManager,
 )
 from sglang.srt.observability.trace import SpanAttributes, extract_trace_headers
+from sglang.srt.runtime_context import (
+    get_device,
+    get_disagg,
+    get_lora,
+    get_model,
+    get_observability,
+    get_serving,
+)
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.server_args import (
     PortArgs,
@@ -463,10 +471,10 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         # TODO: Refactor and organize the log export code.
         # Request logging
         self.request_logger = RequestLogger(
-            log_requests=self.server_args.log_requests,
-            log_requests_level=self.server_args.log_requests_level,
-            log_requests_format=self.server_args.log_requests_format,
-            log_requests_target=self.server_args.log_requests_target,
+            log_requests=get_observability().log_requests,
+            log_requests_level=get_observability().log_requests_level,
+            log_requests_format=get_observability().log_requests_format,
+            log_requests_target=get_observability().log_requests_target,
         )
 
         # Dumping
@@ -489,7 +497,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
     def init_weight_update(self):
         # Initial weights status
         self.initial_weights_loaded = True
-        if self.server_args.checkpoint_engine_wait_weights_before_ready:
+        if get_model().checkpoint_engine_wait_weights_before_ready:
             self.initial_weights_loaded = False
 
         # Weight updates
@@ -509,7 +517,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         # The registry dynamically updates as adapters are loaded / unloaded during runtime. It
         # serves as the source of truth for available adapters and maps user-friendly LoRA names
         # to internally used unique LoRA IDs.
-        self.lora_registry = LoRARegistry(self.server_args.lora_paths)
+        self.lora_registry = LoRARegistry(get_lora().lora_paths)
         # Lock to serialize LoRA update operations.
         # Please note that, unlike `model_update_lock`, this does not block inference, allowing
         # LoRA updates and inference to overlap.
@@ -518,15 +526,13 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         # point to their latest LoRARef objects, so that they can be
         # dynamically loaded if needed for inference
         self.lora_ref_cache: Dict[str, LoRARef] = {}
-        if self.server_args.lora_paths is not None:
-            for lora_ref in self.server_args.lora_paths:
+        if get_lora().lora_paths is not None:
+            for lora_ref in get_lora().lora_paths:
                 self.lora_ref_cache[lora_ref.lora_name] = lora_ref
 
     def init_disaggregation(self):
         # PD Disaggregation
-        self.disaggregation_mode = DisaggregationMode(
-            self.server_args.disaggregation_mode
-        )
+        self.disaggregation_mode = DisaggregationMode(get_disagg().disaggregation_mode)
         # Keep a reference so the bootstrap server is not garbage-collected.
         self.bootstrap_server = start_disagg_service(self.server_args)
         # Single-source counter for auto-assigning fake bootstrap_room.
@@ -535,18 +541,16 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         # Encoder Disaggregation
         self.encoder_bootstrap_server = None
         if self.server_args.language_only:
-            from sglang.srt.disaggregation.encode_receiver import (
-                EncoderBootstrapServer,
-            )
+            from sglang.srt.disaggregation.encode_receiver import EncoderBootstrapServer
 
             # Shared mutable URL list: the bootstrap server appends / removes
             # entries as encoders register, the receiver reads from the same
             # list.  Pre-populated with static --encoder-urls so the legacy
             # CLI flag still works (alongside dynamic registrations).
-            self.encoder_urls: List[str] = list(self.server_args.encoder_urls)
+            self.encoder_urls: List[str] = list(get_disagg().encoder_urls)
             self.encoder_bootstrap_server = EncoderBootstrapServer(
-                host=self.server_args.host,
-                port=self.server_args.encoder_bootstrap_port,
+                host=get_serving().host,
+                port=get_disagg().encoder_bootstrap_port,
                 urls=self.encoder_urls,
             )
             self.mm_receiver = create_mm_receiver(
@@ -560,20 +564,22 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         # Metrics
         if self.enable_metrics:
             engine_type = DisaggregationMode.to_engine_type(
-                self.server_args.disaggregation_mode
+                get_disagg().disaggregation_mode
             )
 
             labels = {
-                "model_name": self.server_args.served_model_name,
+                "model_name": get_serving().served_model_name,
                 "engine_type": engine_type,
             }
             if self.enable_priority_scheduling:
                 labels["priority"] = ""
-            if self.server_args.tokenizer_metrics_allowed_custom_labels:
-                for label in self.server_args.tokenizer_metrics_allowed_custom_labels:
+            if get_observability().tokenizer_metrics_allowed_custom_labels:
+                for (
+                    label
+                ) in get_observability().tokenizer_metrics_allowed_custom_labels:
                     labels[label] = ""
-            if self.server_args.extra_metric_labels:
-                labels.update(self.server_args.extra_metric_labels)
+            if get_observability().extra_metric_labels:
+                labels.update(get_observability().extra_metric_labels)
             tokenizer_collector_cls = resolve_collector_class(
                 self.server_args,
                 STAT_LOGGER_ROLE_TOKENIZER,
@@ -582,18 +588,18 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
             self.metrics_collector = tokenizer_collector_cls(
                 server_args=self.server_args,
                 labels=labels,
-                bucket_time_to_first_token=self.server_args.bucket_time_to_first_token,
-                bucket_e2e_request_latency=self.server_args.bucket_e2e_request_latency,
-                bucket_inter_token_latency=self.server_args.bucket_inter_token_latency,
+                bucket_time_to_first_token=get_observability().bucket_time_to_first_token,
+                bucket_e2e_request_latency=get_observability().bucket_e2e_request_latency,
+                bucket_inter_token_latency=get_observability().bucket_inter_token_latency,
             )
 
             start_cpu_monitor_thread("tokenizer")
 
-        if self.server_args.gc_warning_threshold_secs > 0.0:
-            configure_gc_warning(self.server_args.gc_warning_threshold_secs)
+        if get_observability().gc_warning_threshold_secs > 0.0:
+            configure_gc_warning(get_observability().gc_warning_threshold_secs)
         self.soft_watchdog = Watchdog.create(
             debug_name="TokenizerManager",
-            watchdog_timeout=self.server_args.soft_watchdog_timeout,
+            watchdog_timeout=get_device().soft_watchdog_timeout,
             soft=True,
             test_stuck_time=envs.SGLANG_TEST_STUCK_TOKENIZER.get(),
         )
@@ -1757,7 +1763,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
 
         # default the load format to the server_args
         if obj.load_format is None:
-            obj.load_format = self.server_args.load_format
+            obj.load_format = get_model().load_format
         logger.info("Start update_weights. Load format=%s", obj.load_format)
 
         if obj.abort_all_requests:
@@ -1783,7 +1789,9 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
 
     def _update_model_path_info(self, model_path: str, load_format: str):
         self.served_model_name = model_path
-        self.server_args.override(
+        from sglang.srt.runtime_context import get_context
+
+        get_context().override(
             "tokenizer.update_weights", model_path=model_path, load_format=load_format
         )
         self.model_path = model_path
@@ -1927,7 +1935,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
                 "id": rid,
                 "finish_reason": recv_obj.finished_reasons[i],
                 "prompt_tokens": recv_obj.prompt_tokens[i],
-                "weight_version": self.server_args.weight_version,
+                "weight_version": get_serving().weight_version,
                 "num_retractions": recv_obj.retraction_counts[i],
             }
 
@@ -2801,7 +2809,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
         meta_info = {
             "id": recv_obj.rid,
             "finish_reason": finish_reason,
-            "weight_version": self.server_args.weight_version,
+            "weight_version": get_serving().weight_version,
             "e2e_latency": state.time_stats.get_e2e_latency(),
         }
         is_stream = getattr(state.obj, "stream", False)

python/sglang/srt/managers/tokenizer_manager_score_mixin.py

@@ -597,7 +597,10 @@ class TokenizerManagerScoreMixin:
                         f"Token ID {token_id} is out of vocabulary (vocab size: {vocab_size})"
                     )
 
-        # Check if multi-item scoring is enabled
+        # Check if multi-item scoring is enabled. enable_mis is a static startup
+        # feature flag (never overridden post-publish), and score_request is also
+        # exercised on a bare mixin without a published context, so read it off
+        # server_args rather than the resolved-config bag.
         use_multi_item_scoring = self.server_args.enable_mis
 
         input_ids = None

python/sglang/srt/managers/tp_worker.py

@@ -47,6 +47,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     PPProxyTensors,
 )
 from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
+from sglang.srt.runtime_context import get_exec, get_model, get_schedule, get_spec
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import MultiprocessingSerializer, broadcast_pyobj, set_random_seed
 from sglang.srt.utils.hf_transformers_utils import (
@@ -405,14 +406,14 @@ class TpModelWorker(BaseTpWorker):
         self.model_config = ModelConfig.from_server_args(
             self.server_args,
             model_path=(
-                self.server_args.model_path
+                get_model().model_path
                 if not self.is_draft_worker
-                else self.server_args.speculative_draft_model_path
+                else get_spec().speculative_draft_model_path
             ),
             model_revision=(
-                self.server_args.revision
+                get_model().revision
                 if not self.is_draft_worker
-                else self.server_args.speculative_draft_model_revision
+                else get_spec().speculative_draft_model_revision
             ),
             is_draft_model=self.is_draft_worker,
             context_length=self.context_length,
@@ -423,7 +424,7 @@ class TpModelWorker(BaseTpWorker):
 
         self._model_runner = ModelRunner(
             model_config=self.model_config,
-            mem_fraction_static=self.server_args.mem_fraction_static,
+            mem_fraction_static=get_schedule().mem_fraction_static,
             gpu_id=self.gpu_id,
             ps=self.ps,
             nccl_port=self.nccl_port,
@@ -439,11 +440,11 @@ class TpModelWorker(BaseTpWorker):
         from sglang.srt.model_executor.model_runner import ModelRunner
 
         self.model_runner_list.append(self.model_runner)
-        for i in range(1, self.server_args.speculative_num_steps):
+        for i in range(1, get_spec().speculative_num_steps):
             self.model_runner_list.append(
                 ModelRunner(
                     model_config=self.model_config,
-                    mem_fraction_static=self.server_args.mem_fraction_static,
+                    mem_fraction_static=get_schedule().mem_fraction_static,
                     gpu_id=self.gpu_id,
                     ps=self.ps,
                     nccl_port=self.nccl_port,
@@ -459,7 +460,7 @@ class TpModelWorker(BaseTpWorker):
     def _init_dllm_algorithm(self):
         from sglang.srt.dllm.algorithm.base import DllmAlgorithm
 
-        if self.server_args.dllm_algorithm is not None:
+        if get_exec().dllm.dllm_algorithm is not None:
             self.dllm_algorithm = DllmAlgorithm.from_server_args(self.server_args)
         else:
             self.dllm_algorithm = None
@@ -485,9 +486,9 @@ class TpModelWorker(BaseTpWorker):
         )
         return (
             self.model_runner.max_total_num_tokens,
-            self.server_args.max_prefill_tokens,
+            get_schedule().max_prefill_tokens,
             self.model_runner.max_running_requests,
-            self.server_args.max_queued_requests,
+            get_schedule().max_queued_requests,
             max_req_len,
             max_req_len - 5,
             self.random_seed,

python/sglang/srt/mem_cache/allocation.py

@@ -26,7 +26,7 @@ from sglang.srt.mem_cache.common import (
     evict_from_tree_cache,
 )
 from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec, get_server_args
 from sglang.srt.utils import (
     is_cpu,
     is_cuda,
@@ -65,7 +65,7 @@ def write_cache_indices(
     prefix_tensors: list[torch.Tensor],
     req_to_token_pool: ReqToTokenPool,
 ):
-    if support_triton(get_server_args().attention_backend):
+    if support_triton(get_exec().kernel.attention_backend):
         prefix_pointers = torch.tensor(
             [t.data_ptr() for t in prefix_tensors],
             dtype=torch.uint64,
@@ -106,7 +106,7 @@ def get_last_loc(
     req_pool_indices_tensor: torch.Tensor,
     prefix_lens_tensor: torch.Tensor,
 ) -> torch.Tensor:
-    attn_backend = get_server_args().attention_backend
+    attn_backend = get_exec().kernel.attention_backend
     uses_triton_dispatch = attn_backend not in ("ascend", "torch_native")
 
     if _is_hip and uses_triton_dispatch:

python/sglang/srt/mem_cache/common.py

@@ -16,7 +16,7 @@ from sglang.srt.hardware_backend.npu.dsv4.dsv4_common_hooks import (
 from sglang.srt.mem_cache.allocator.swa import SWATokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache, EvictParams
 from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_server_args, get_serving
 from sglang.srt.utils.common import ceil_align
 
 if TYPE_CHECKING:
@@ -183,7 +183,7 @@ def _release_overallocated_kv_indices(
 
     # strip_thinking_cache intentionally reports output tokens as overallocated
     # so they fall into the free path below (#22373).
-    if spec_algo is None and not global_server_args.strip_thinking_cache:
+    if spec_algo is None and not get_serving().strip_thinking_cache:
         assert (
             start_p == end_p
         ), f"Unexpected overallocated KV cache, {req.kv_committed_len=}, {req.kv.kv_allocated_len=}"

python/sglang/srt/mem_cache/deepseek_v4_memory_pool.py

@@ -21,7 +21,7 @@ from sglang.srt.environ import envs
 from sglang.srt.mem_cache.base_swa_memory_pool import BaseSWAKVPool
 from sglang.srt.mem_cache.deepseek_v4_compress_state import CompressStatePool
 from sglang.srt.mem_cache.memory_pool import KVCache
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec, get_server_args
 from sglang.srt.utils import ceil_div, is_hip
 
 logger = logging.getLogger(__name__)
@@ -276,7 +276,7 @@ class DeepSeekV4IndexerPool(KVCache):
             end_layer,
         )
         self.index_head_dim = index_head_dim
-        self.use_fp4_indexer = get_server_args().enable_deepseek_v4_fp4_indexer
+        self.use_fp4_indexer = get_exec().kernel.enable_deepseek_v4_fp4_indexer
 
         self._create_buffer()
 

python/sglang/srt/mem_cache/kv_cache_configurator.py

@@ -58,7 +58,15 @@ from sglang.srt.mem_cache.memory_pool import (
 )
 from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool
 from sglang.srt.platforms import current_platform
-from sglang.srt.runtime_context import get_model, get_parallel
+from sglang.srt.runtime_context import (
+    get_disagg,
+    get_exec,
+    get_memory,
+    get_model,
+    get_parallel,
+    get_schedule,
+    get_spec,
+)
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils.common import (
@@ -115,9 +123,7 @@ if TYPE_CHECKING:
     from sglang.srt.model_executor.model_runner_components.spec_aux_hidden_state import (
         SpecAuxHiddenStateConfig,
     )
-    from sglang.srt.model_executor.pool_configurator import (
-        MemoryPoolConfig,
-    )
+    from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
 
 
 class KVCacheConfigResult(msgspec.Struct, frozen=True, kw_only=True):
@@ -308,8 +314,8 @@ class KVCacheConfigurator:
         # from one byte buffer, then return. Gated to the target worker
         # (req_to_token_pool is None); supports hybrid Mamba and hybrid SWA (not DSV4).
         if (
-            self.server_args.enable_unified_memory
-            and self.server_args.disaggregation_mode == "null"
+            get_memory().enable_unified_memory
+            and get_disagg().disaggregation_mode == "null"
             and req_to_token_pool is None
         ):
             if self.mambaish_config is not None:
@@ -358,13 +364,13 @@ class KVCacheConfigurator:
                 # TARGET_VERIFY, so their pools skip the per-step intermediate
                 # (SpeculativeState) buffers only the target pool consumes.
                 req_to_token_pool = req_to_token_pool.clone_with_new_mamba(
-                    mamba_size=self.server_args.max_mamba_cache_size,
+                    mamba_size=get_schedule().max_mamba_cache_size,
                     mamba_spec_state_size=sizes.max_running_requests,
                     cache_params=self.mambaish_config.mamba2_cache_params,
                     device=self.device,
                     enable_mamba_extra_buffer=self.server_args.enable_mamba_extra_buffer(),
                     draft_model_idx=self.draft_model_idx,
-                    speculative_eagle_topk=self.server_args.speculative_eagle_topk,
+                    speculative_eagle_topk=get_spec().speculative_eagle_topk,
                 )
 
         # Initialize token_to_kv_pool
@@ -394,7 +400,7 @@ class KVCacheConfigurator:
         # unsupported pool families before allocation. Keep this guard here so
         # future pool-selection refactors fail at boot instead of on first use.
         if (
-            self.server_args.prefill_only_disable_kv_cache
+            get_schedule().prefill_only_disable_kv_cache
             and not self.is_draft_worker
             and not isinstance(token_to_kv_pool, NoOpMHATokenToKVPool)
         ):
@@ -432,8 +438,8 @@ class KVCacheConfigurator:
         assert self.page_size >= 1, f"page_size must be >= 1, got {self.page_size}"
         # Mirror the non-shared path's extra_max_context_len computation.
         extra_max_context_len = 4
-        if self.server_args.speculative_num_draft_tokens is not None:
-            extra_max_context_len += self.server_args.speculative_num_draft_tokens
+        if get_spec().speculative_num_draft_tokens is not None:
+            extra_max_context_len += get_spec().speculative_num_draft_tokens
 
         mamba_layer_ids = [
             i
@@ -462,14 +468,14 @@ class KVCacheConfigurator:
             model_context_len=self.model_config.context_len,
             extra_max_context_len=extra_max_context_len,
             max_total_num_tokens=max_total_num_tokens,
-            max_mamba_cache_size=self.server_args.max_mamba_cache_size,
+            max_mamba_cache_size=get_schedule().max_mamba_cache_size,
             max_num_reqs=max_num_reqs,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             enable_mamba_extra_buffer=self.server_args.enable_mamba_extra_buffer(),
-            speculative_num_draft_tokens=self.server_args.speculative_num_draft_tokens,
-            disable_overlap_schedule=self.server_args.disable_overlap_schedule,
-            need_sort=self.server_args.disaggregation_mode in ("decode", "prefill"),
-            mamba_full_memory_ratio=self.server_args.mamba_full_memory_ratio,
+            speculative_num_draft_tokens=get_spec().speculative_num_draft_tokens,
+            disable_overlap_schedule=get_schedule().disable_overlap_schedule,
+            need_sort=get_disagg().disaggregation_mode in ("decode", "prefill"),
+            mamba_full_memory_ratio=get_schedule().mamba_full_memory_ratio,
             # Overlap mode: the allocator's `free` drops a wait_stream(forward_stream)
             # barrier so eager compaction serializes after the in-flight forward's
             # v2p/KV reads. Near-no-op in normal mode.
@@ -502,13 +508,13 @@ class KVCacheConfigurator:
         ), "unified memory pool does not support MLA-SWA hybrid yet"
         # Mirror the non-shared path's extra_max_context_len computation.
         extra_max_context_len = 4
-        if self.server_args.speculative_num_draft_tokens is not None:
-            extra_max_context_len += self.server_args.speculative_num_draft_tokens
+        if get_spec().speculative_num_draft_tokens is not None:
+            extra_max_context_len += get_spec().speculative_num_draft_tokens
         req_to_token_pool = ReqToTokenPool(
             size=max_num_reqs,
             max_context_len=self.model_config.context_len + extra_max_context_len,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
         )
 
         head_num = self.model_config.get_num_kv_heads(get_parallel().attn_tp_size)
@@ -558,8 +564,8 @@ class KVCacheConfigurator:
             full_attention_layer_ids=full_attention_layer_ids,
             full_max_total_num_tokens=full_max_total_num_tokens,
             swa_max_total_num_tokens=swa_max_total_num_tokens,
-            enable_memory_saver=self.server_args.enable_memory_saver,
-            need_sort=self.server_args.disaggregation_mode in ("decode", "prefill"),
+            enable_memory_saver=get_exec().features.enable_memory_saver,
+            need_sort=get_disagg().disaggregation_mode in ("decode", "prefill"),
             # Overlap mode: same wait_stream(forward_stream) rationale as
             # `_init_unified_mamba_pools`.
             forward_stream=self.forward_stream,
@@ -579,7 +585,7 @@ class KVCacheConfigurator:
         is_dsv4_model: bool,
         current_platform,
     ):
-        if not self.server_args.prefill_only_disable_kv_cache or self.is_draft_worker:
+        if not get_schedule().prefill_only_disable_kv_cache or self.is_draft_worker:
             return
 
         unsupported_pool_family = None
@@ -588,7 +594,7 @@ class KVCacheConfigurator:
         elif current_platform.is_out_of_tree() and not self.mambaish_config:
             unsupported_pool_family = "out-of-tree platform KV pool"
         elif (
-            self.server_args.attention_backend == "ascend" and not self.mambaish_config
+            get_exec().kernel.attention_backend == "ascend" and not self.mambaish_config
         ):
             unsupported_pool_family = "NPU/Ascend KV pool"
         elif self.use_mla_backend and is_dsa_model:
@@ -614,9 +620,9 @@ class KVCacheConfigurator:
     def _build_req_to_token_pool(self, *, max_num_reqs: int) -> ReqToTokenPool:
         extra_max_context_len = get_req_to_token_extra_context_len(self.server_args)
 
-        if self.server_args.disaggregation_mode == "decode":
+        if get_disagg().disaggregation_mode == "decode":
             # Extra slots for pre-allocated requests
-            pre_alloc_size = self.server_args.disaggregation_decode_extra_slots
+            pre_alloc_size = get_disagg().disaggregation_decode_extra_slots
             if self.mambaish_config:
                 req_to_token_pool = self._build_hybrid_mamba_decode_req_pool(
                     max_num_reqs=max_num_reqs,
@@ -648,15 +654,13 @@ class KVCacheConfigurator:
         extra_max_context_len: int,
         pre_alloc_size: int,
     ) -> ReqToTokenPool:
-        from sglang.srt.disaggregation.decode import (
-            HybridMambaDecodeReqToTokenPool,
-        )
+        from sglang.srt.disaggregation.decode import HybridMambaDecodeReqToTokenPool
 
         req_to_token_pool = HybridMambaDecodeReqToTokenPool(
             size=max_num_reqs,
             max_context_len=self.model_config.context_len + extra_max_context_len,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             cache_params=self.mambaish_config.mamba2_cache_params,
             mamba_layer_ids=(
                 [
@@ -666,11 +670,11 @@ class KVCacheConfigurator:
                 ]
             ),
             speculative_num_draft_tokens=self.server_args.max_speculative_num_draft_tokens,
-            speculative_eagle_topk=self.server_args.speculative_eagle_topk,
+            speculative_eagle_topk=get_spec().speculative_eagle_topk,
             enable_mamba_extra_buffer=self.server_args.enable_mamba_extra_buffer(),
             pre_alloc_size=pre_alloc_size,
-            enable_overlap_schedule=not self.server_args.disable_overlap_schedule,
-            mamba_size=self.server_args.max_mamba_cache_size,
+            enable_overlap_schedule=not get_schedule().disable_overlap_schedule,
+            mamba_size=get_schedule().max_mamba_cache_size,
             start_layer=self.layer_info.start_layer,
         )
         return req_to_token_pool
@@ -688,7 +692,7 @@ class KVCacheConfigurator:
             size=max_num_reqs,
             max_context_len=self.model_config.context_len + extra_max_context_len,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             pre_alloc_size=pre_alloc_size,
         )
         return req_to_token_pool
@@ -701,11 +705,11 @@ class KVCacheConfigurator:
     ) -> ReqToTokenPool:
         req_to_token_pool = HybridReqToTokenPool(
             size=max_num_reqs,
-            mamba_size=self.server_args.max_mamba_cache_size,
+            mamba_size=get_schedule().max_mamba_cache_size,
             mamba_spec_state_size=max_num_reqs,
             max_context_len=self.model_config.context_len + extra_max_context_len,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             cache_params=self.mambaish_config.mamba2_cache_params,
             mamba_layer_ids=(
                 [
@@ -717,18 +721,18 @@ class KVCacheConfigurator:
             enable_mamba_extra_buffer=self.server_args.enable_mamba_extra_buffer(),
             enable_mamba_extra_buffer_lazy=self.server_args.enable_mamba_extra_buffer_lazy(),
             speculative_num_draft_tokens=self.server_args.max_speculative_num_draft_tokens,
-            speculative_eagle_topk=self.server_args.speculative_eagle_topk,
-            enable_overlap_schedule=not self.server_args.disable_overlap_schedule,
+            speculative_eagle_topk=get_spec().speculative_eagle_topk,
+            enable_overlap_schedule=not get_schedule().disable_overlap_schedule,
             start_layer=self.layer_info.start_layer,
-            enable_linear_replayssm=self.server_args.enable_linear_replayssm,
-            linear_replayssm_cache_len=self.server_args.linear_replayssm_cache_len,
-            mamba_envelope_layout=self.server_args.enable_page_major_kv_layout,
+            enable_linear_replayssm=get_exec().mamba.enable_linear_replayssm,
+            linear_replayssm_cache_len=get_exec().mamba.linear_replayssm_cache_len,
+            mamba_envelope_layout=get_memory().enable_page_major_kv_layout,
             # ReplaySSM spec-verify is GDN-only: activate the pool machinery
             # (rings + cursors + the intermediate_ssm gate) only for GDN-hybrid
             # models, so any other mamba-ish model (Mamba2/Nemotron, lightning,
             # ...) run with the flag set stays byte-identical to flag-off.
             enable_gdn_replayssm_spec=(
-                self.server_args.enable_gdn_replayssm_spec
+                get_exec().mamba.enable_gdn_replayssm_spec
                 and self.hybrid_gdn_config is not None
             ),
         )
@@ -754,7 +758,7 @@ class KVCacheConfigurator:
             size=max_num_reqs,
             max_context_len=self.model_config.context_len + extra_max_context_len,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
         )
         return req_to_token_pool
 
@@ -770,7 +774,7 @@ class KVCacheConfigurator:
         # selected by swapping in the PageMajorMHATokenToKVPool subclass. The
         # default keeps upstream's per-layer layout. The Mamba state pool is routed
         # separately via `mamba_envelope_layout` on the req-to-token pool above.
-        enable_page_major = self.server_args.enable_page_major_kv_layout
+        enable_page_major = get_memory().enable_page_major_kv_layout
         mha_pool_class = (
             PageMajorMHATokenToKVPool if enable_page_major else MHATokenToKVPool
         )
@@ -802,7 +806,7 @@ class KVCacheConfigurator:
                     max_total_num_tokens=sizes.max_total_num_tokens,
                 )
         elif (
-            self.server_args.attention_backend == "ascend" and not self.mambaish_config
+            get_exec().kernel.attention_backend == "ascend" and not self.mambaish_config
         ):
             if self.is_hybrid_swa:
                 token_to_kv_pool = self._build_ascend_swa_kv_pool(
@@ -878,14 +882,12 @@ class KVCacheConfigurator:
         c128_state_dtype: Optional[torch.dtype],
         req_to_token_pool: ReqToTokenPool,
     ) -> KVCache:
-        swa_page_size = self.server_args.page_size
+        swa_page_size = get_schedule().page_size
         if not _is_npu:
             assert swa_page_size == 256, "In paged swa mode, page_size must be 256."
 
         if self.is_draft_worker:
-            from sglang.srt.models.deepseek_v4_nextn import (
-                COMPRESS_RATIO_NEXTN_LAYER,
-            )
+            from sglang.srt.models.deepseek_v4_nextn import COMPRESS_RATIO_NEXTN_LAYER
 
             compression_ratios = [
                 COMPRESS_RATIO_NEXTN_LAYER
@@ -912,12 +914,12 @@ class KVCacheConfigurator:
             # sliding eviction in ``ScheduleBatch._evict_swa``.
             c4_state_pool_size = npu_state_pool_size(
                 ratio=4,
-                page_size=self.server_args.page_size,
+                page_size=get_schedule().page_size,
                 max_num_reqs=max_running_requests,
             )
             c128_state_pool_size = npu_state_pool_size(
                 ratio=128,
-                page_size=self.server_args.page_size,
+                page_size=get_schedule().page_size,
                 max_num_reqs=max_running_requests,
             )
         else:
@@ -935,7 +937,7 @@ class KVCacheConfigurator:
             c128_size=c128_max_total_num_tokens,
             c4_state_pool_size=c4_state_pool_size,
             c128_state_pool_size=c128_state_pool_size,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             swa_page_size=swa_page_size,
             sliding_window=self.model_config.window_size,
             dtype=self.kv_cache_dtype,
@@ -946,11 +948,11 @@ class KVCacheConfigurator:
             indexer_head_dim=self.model_config.index_head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             compression_ratios=compression_ratios,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
-            enable_hisparse=self.server_args.enable_hisparse,
+            enable_hisparse=get_memory().enable_hisparse,
             online_mtp_max_draft_tokens=(
                 self.server_args.max_speculative_num_draft_tokens or 0
             ),
@@ -961,7 +963,7 @@ class KVCacheConfigurator:
         PoolCls = current_platform.get_dsa_kv_pool_cls()
         token_to_kv_pool = PoolCls(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
@@ -972,7 +974,7 @@ class KVCacheConfigurator:
                 kv_cache_dtype=self.kv_cache_dtype,
                 server_args=self.server_args,
             ),
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
             index_head_dim=get_dsa_index_head_dim(self.model_config.hf_config),
@@ -985,14 +987,14 @@ class KVCacheConfigurator:
         PoolCls = current_platform.get_mla_kv_pool_cls()
         token_to_kv_pool = PoolCls(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
             index_head_dim=(self.model_config.index_head_dim if is_dsa_model else None),
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1002,13 +1004,13 @@ class KVCacheConfigurator:
         PoolCls = current_platform.get_mha_kv_pool_cls()
         token_to_kv_pool = PoolCls(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
             head_dim=self.model_config.head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1020,9 +1022,7 @@ class KVCacheConfigurator:
         full_max_total_num_tokens: Optional[int],
         swa_max_total_num_tokens: Optional[int],
     ) -> KVCache:
-        from sglang.srt.hardware_backend.npu.memory_pool_npu import (
-            NPUMHATokenToKVPool,
-        )
+        from sglang.srt.hardware_backend.npu.memory_pool_npu import NPUMHATokenToKVPool
 
         kwargs = {}
         if self.is_hybrid_swa_compress:
@@ -1039,7 +1039,7 @@ class KVCacheConfigurator:
         token_to_kv_pool = SWAKVPool(
             size=full_max_total_num_tokens,
             size_swa=swa_max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             post_capture_active=self.post_capture_kv_active,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
@@ -1055,39 +1055,35 @@ class KVCacheConfigurator:
     def _build_ascend_mla_kv_pool(
         self, *, max_total_num_tokens: int, is_dsa_model: bool
     ) -> KVCache:
-        from sglang.srt.hardware_backend.npu.memory_pool_npu import (
-            NPUMLATokenToKVPool,
-        )
+        from sglang.srt.hardware_backend.npu.memory_pool_npu import NPUMLATokenToKVPool
 
         token_to_kv_pool = NPUMLATokenToKVPool(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
             index_head_dim=(self.model_config.index_head_dim if is_dsa_model else None),
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
         return token_to_kv_pool
 
     def _build_ascend_mha_kv_pool(self, *, max_total_num_tokens: int) -> KVCache:
-        from sglang.srt.hardware_backend.npu.memory_pool_npu import (
-            NPUMHATokenToKVPool,
-        )
+        from sglang.srt.hardware_backend.npu.memory_pool_npu import NPUMHATokenToKVPool
 
         token_to_kv_pool = NPUMHATokenToKVPool(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
             head_dim=self.model_config.head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1101,7 +1097,7 @@ class KVCacheConfigurator:
             dsa_cp_layer_shard_size,
         ) = get_glm_dsa_cp_layer_shard_info(self)
         pool_kwargs = {}
-        if self.server_args.enable_hisparse:
+        if get_memory().enable_hisparse:
             PoolCls = HiSparseDSATokenToKVPool
             from sglang.srt.mem_cache.sparsity import parse_hisparse_config
 
@@ -1121,7 +1117,7 @@ class KVCacheConfigurator:
             PoolCls = DSATokenToKVPool
         token_to_kv_pool = PoolCls(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
@@ -1132,7 +1128,7 @@ class KVCacheConfigurator:
                 kv_cache_dtype=self.kv_cache_dtype,
                 server_args=self.server_args,
             ),
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
             index_head_dim=get_dsa_index_head_dim(self.model_config.hf_config),
@@ -1143,13 +1139,13 @@ class KVCacheConfigurator:
     def _build_mla_fp4_kv_pool(self, *, max_total_num_tokens: int) -> KVCache:
         token_to_kv_pool = MLATokenToKVPoolFP4(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1158,13 +1154,13 @@ class KVCacheConfigurator:
     def _build_mla_kv_pool(self, *, max_total_num_tokens: int) -> KVCache:
         token_to_kv_pool = MLATokenToKVPool(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             kv_lora_rank=self.model_config.kv_lora_rank,
             qk_rope_head_dim=self.model_config.qk_rope_head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1221,7 +1217,7 @@ class KVCacheConfigurator:
         token_to_kv_pool = SWAKVPool(
             size=full_max_total_num_tokens,
             size_swa=size_swa,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             post_capture_active=self.post_capture_kv_active,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
@@ -1229,7 +1225,7 @@ class KVCacheConfigurator:
             swa_attention_layer_ids=swa_attention_layer_ids,
             full_attention_layer_ids=full_attention_layer_ids,
             device=self.device,
-            enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None),
+            enable_kv_cache_copy=(get_spec().speculative_algorithm is not None),
             token_to_kv_pool_class=swa_pool_class,
             **kwargs,
         )
@@ -1244,7 +1240,7 @@ class KVCacheConfigurator:
         )
         token_to_kv_pool = MiniMaxSparseKVPool(
             size=max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             index_dtype=self.model_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
@@ -1254,7 +1250,7 @@ class KVCacheConfigurator:
             sparse_layer_ids=sparse_layer_ids,
             disable_value_sparse_layer_ids=disable_value_sparse_layer_ids,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
         )
@@ -1293,7 +1289,7 @@ class KVCacheConfigurator:
             else mha_pool_class
         )
         token_to_kv_pool = HybridLinearKVPool(
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             size=max_total_num_tokens,
             dtype=self.kv_cache_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
@@ -1302,8 +1298,8 @@ class KVCacheConfigurator:
             full_attention_layer_ids=full_attention_layer_ids,
             device=self.device,
             mamba_pool=req_to_token_pool.mamba_pool,
-            enable_memory_saver=self.server_args.enable_memory_saver,
-            enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None),
+            enable_memory_saver=get_exec().features.enable_memory_saver,
+            enable_kv_cache_copy=(get_spec().speculative_algorithm is not None),
             use_mla=self.use_mla_backend,
             start_layer=self.layer_info.start_layer,
             full_kv_pool_class=full_pool_class,
@@ -1316,18 +1312,18 @@ class KVCacheConfigurator:
     def _build_mha_fp4_kv_pool(self, *, max_total_num_tokens: int) -> KVCache:
         token_to_kv_pool = MHATokenToKVPoolFP4(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
             head_dim=self.model_config.head_dim,
             v_head_dim=self.model_config.v_head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
-            enable_alt_stream=not self.server_args.enable_pdmux,
-            enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None),
+            enable_alt_stream=not get_disagg().enable_pdmux,
+            enable_kv_cache_copy=(get_spec().speculative_algorithm is not None),
         )
         return token_to_kv_pool
 
@@ -1339,7 +1335,7 @@ class KVCacheConfigurator:
         else:
             pool_cls = (
                 NoOpMHATokenToKVPool
-                if self.server_args.prefill_only_disable_kv_cache
+                if get_schedule().prefill_only_disable_kv_cache
                 else mha_pool_class
             )
         pool_kwargs = {}
@@ -1349,18 +1345,18 @@ class KVCacheConfigurator:
             pool_kwargs["post_capture_active"] = self.post_capture_kv_active
         token_to_kv_pool = pool_cls(
             max_total_num_tokens,
-            page_size=self.server_args.page_size,
+            page_size=get_schedule().page_size,
             dtype=self.kv_cache_dtype,
             head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size),
             head_dim=self.model_config.head_dim,
             v_head_dim=self.model_config.v_head_dim,
             layer_num=self.layer_info.num_effective_layers,
             device=self.device,
-            enable_memory_saver=self.server_args.enable_memory_saver,
+            enable_memory_saver=get_exec().features.enable_memory_saver,
             start_layer=self.layer_info.start_layer,
             end_layer=self.layer_info.end_layer,
-            enable_alt_stream=not self.server_args.enable_pdmux,
-            enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None),
+            enable_alt_stream=not get_disagg().enable_pdmux,
+            enable_kv_cache_copy=(get_spec().speculative_algorithm is not None),
             **pool_kwargs,
         )
         return token_to_kv_pool
@@ -1375,20 +1371,20 @@ class KVCacheConfigurator:
         token_to_kv_pool_allocator: Optional[BaseTokenToKVPoolAllocator],
     ) -> BaseTokenToKVPoolAllocator:
         # Initialize token_to_kv_pool_allocator
-        need_sort = self.server_args.disaggregation_mode in ("decode", "prefill")
+        need_sort = get_disagg().disaggregation_mode in ("decode", "prefill")
         if token_to_kv_pool_allocator is None:
             if current_platform.is_out_of_tree():
                 AllocatorCls = current_platform.get_paged_allocator_cls()
                 token_to_kv_pool_allocator = AllocatorCls(
                     sizes.max_total_num_tokens,
-                    page_size=self.server_args.page_size,
+                    page_size=get_schedule().page_size,
                     dtype=self.kv_cache_dtype,
                     device=self.device,
                     kvcache=token_to_kv_pool,
                     need_sort=need_sort,
                 )
             elif _is_npu and (
-                self.server_args.attention_backend == "ascend"
+                get_exec().kernel.attention_backend == "ascend"
                 or is_dsv4_model
                 or self.hybrid_gdn_config is not None
             ):
@@ -1406,7 +1402,7 @@ class KVCacheConfigurator:
                     token_to_kv_pool_allocator = swa_allocator_cls(
                         sizes.full_max_total_num_tokens,
                         sizes.swa_max_total_num_tokens,
-                        page_size=self.server_args.page_size,
+                        page_size=get_schedule().page_size,
                         dtype=self.kv_cache_dtype,
                         device=self.device,
                         kvcache=token_to_kv_pool,
@@ -1419,7 +1415,7 @@ class KVCacheConfigurator:
 
                     token_to_kv_pool_allocator = NPUPagedTokenToKVPoolAllocator(
                         sizes.max_total_num_tokens,
-                        page_size=self.server_args.page_size,
+                        page_size=get_schedule().page_size,
                         dtype=self.kv_cache_dtype,
                         device=self.device,
                         kvcache=token_to_kv_pool,
@@ -1429,7 +1425,7 @@ class KVCacheConfigurator:
                 if self.is_hybrid_swa and sizes.full_max_total_num_tokens == 0:
                     token_to_kv_pool_allocator = PureSWATokenToKVPoolAllocator(
                         sizes.swa_max_total_num_tokens,
-                        page_size=self.server_args.page_size,
+                        page_size=get_schedule().page_size,
                         dtype=self.kv_cache_dtype,
                         device=self.device,
                         kvcache=token_to_kv_pool,
@@ -1439,22 +1435,20 @@ class KVCacheConfigurator:
                     token_to_kv_pool_allocator = SWATokenToKVPoolAllocator(
                         sizes.full_max_total_num_tokens,
                         sizes.swa_max_total_num_tokens,
-                        page_size=self.server_args.page_size,
+                        page_size=get_schedule().page_size,
                         dtype=self.kv_cache_dtype,
                         device=self.device,
                         kvcache=token_to_kv_pool,
                         need_sort=need_sort,
                     )
                 else:
-                    if self.server_args.enable_hisparse:
-                        from sglang.srt.mem_cache.sparsity import (
-                            parse_hisparse_config,
-                        )
+                    if get_memory().enable_hisparse:
+                        from sglang.srt.mem_cache.sparsity import parse_hisparse_config
 
                         hisparse_cfg = parse_hisparse_config(self.server_args)
                         token_to_kv_pool_allocator = HiSparseTokenToKVPoolAllocator(
                             sizes.max_total_num_tokens,
-                            page_size=self.server_args.page_size,
+                            page_size=get_schedule().page_size,
                             dtype=self.kv_cache_dtype,
                             device=self.device,
                             kvcache=token_to_kv_pool,
@@ -1462,8 +1456,7 @@ class KVCacheConfigurator:
                             host_to_device_ratio=hisparse_cfg.host_to_device_ratio,
                         )
                     elif (
-                        self.server_args.page_size == 1
-                        and self.server_args.dcp_size == 1
+                        get_schedule().page_size == 1 and self.server_args.dcp_size == 1
                     ):
                         token_to_kv_pool_allocator = TokenToKVPoolAllocator(
                             sizes.max_total_num_tokens,
@@ -1475,7 +1468,7 @@ class KVCacheConfigurator:
                     else:
                         token_to_kv_pool_allocator = PagedTokenToKVPoolAllocator(
                             sizes.max_total_num_tokens * self.server_args.dcp_size,
-                            page_size=self.server_args.page_size
+                            page_size=get_schedule().page_size
                             * self.server_args.dcp_size,
                             dtype=self.kv_cache_dtype,
                             device=self.device,
@@ -1483,7 +1476,7 @@ class KVCacheConfigurator:
                             need_sort=need_sort,
                         )
 
-            if self.server_args.enable_hisparse and is_dsv4_model:
+            if get_memory().enable_hisparse and is_dsv4_model:
                 assert self.is_hybrid_swa, "DeepSeek V4 HiSparse requires SWA mode."
                 token_to_kv_pool_allocator = DeepSeekV4HiSparseTokenToKVPoolAllocator(
                     token_to_kv_pool_allocator
@@ -1535,7 +1528,7 @@ class KVCacheConfigurator:
             cpu_group=get_world_group().cpu_group,
         )
 
-        slack_gb = pre_model_load_memory * (1 - self.server_args.mem_fraction_static)
+        slack_gb = pre_model_load_memory * (1 - get_schedule().mem_fraction_static)
         if self.mambaish_config is not None and self.post_capture_kv_active:
             # Mamba state is a fixed pre-capture allocation, so it can't ride the ~0 post-capture slack.
             slack_gb = max(
@@ -1559,7 +1552,7 @@ class KVCacheConfigurator:
             )
             raise ValueError(
                 f"Loaded weights leave no GPU memory for the KV cache under "
-                f"--mem-fraction-static={self.server_args.mem_fraction_static}. "
+                f"--mem-fraction-static={get_schedule().mem_fraction_static}. "
                 f"Raise --mem-fraction-static above "
                 f"{suggested_mem_fraction_static:.3f} "
                 f"(minimum viable = 1 - available/pre = "
@@ -1570,14 +1563,14 @@ class KVCacheConfigurator:
         return int(rest_memory * (1 << 30))  # return in bytes
 
     def _calculate_mamba_ratio(self) -> int:
-        if self.server_args.disable_radix_cache:
+        if get_memory().disable_radix_cache:
             return 1
 
         additional_ratio = 0
         if self.server_args.enable_mamba_extra_buffer():
             # ping-pong buffer size is 2 when overlap schedule is on, 1 otherwise.
             # Lazy mode saves 1 slot (2 → 1) for overlap; non-overlap already uses 1.
-            if not self.server_args.disable_overlap_schedule:
+            if not get_schedule().disable_overlap_schedule:
                 if self.server_args.enable_mamba_extra_buffer_lazy():
                     additional_ratio = MAMBA_CACHE_V2_ADDITIONAL_RATIO_OVERLAP_LAZY
                 else:
@@ -1596,7 +1589,7 @@ class KVCacheConfigurator:
         Page alignment is handled by the configurator, not here.
         If constraints change the value, the configurator re-runs and re-aligns.
         """
-        user_limit = self.server_args.max_total_tokens
+        user_limit = get_schedule().max_total_tokens
 
         # Apply user-specified upper bound
         if user_limit is not None:
@@ -1626,7 +1619,7 @@ class KVCacheConfigurator:
         estimated = int(token_capacity / self.model_config.context_len * 512)
         estimated = max(min(estimated, 4096), 2048)
 
-        max_num_reqs = self.server_args.max_running_requests
+        max_num_reqs = get_schedule().max_running_requests
         if max_num_reqs is not None:
             requested_per_worker = max_num_reqs // self.ps.attn_dp_size
             max_num_reqs = min(requested_per_worker, token_capacity // 2)
@@ -1637,13 +1630,13 @@ class KVCacheConfigurator:
         if self.mambaish_config is not None:
             ratio = self._calculate_mamba_ratio()
             max_num_reqs = min(
-                max_num_reqs, self.server_args.max_mamba_cache_size // ratio
+                max_num_reqs, get_schedule().max_mamba_cache_size // ratio
             )
 
             if max_num_reqs <= 0:
                 raise RuntimeError(
                     f"Hybrid (mamba/linear-attention) state cache is too small to serve "
-                    f"any requests. max_mamba_cache_size={self.server_args.max_mamba_cache_size}, "
+                    f"any requests. max_mamba_cache_size={get_schedule().max_mamba_cache_size}, "
                     f"mamba_ratio={ratio}, resulting max_num_reqs={max_num_reqs}. "
                     f"Try: (1) reduce --max-running-requests, "
                     f"(2) increase --mem-fraction-static, or "
@@ -1673,7 +1666,7 @@ class KVCacheConfigurator:
         )
         configurator = create_memory_pool_configurator(self)
         config = configurator.finalize_with_max_running_requests(config)
-        config.mem_fraction_static = self.server_args.mem_fraction_static
+        config.mem_fraction_static = get_schedule().mem_fraction_static
         return config
 
     def config_from_budget(
@@ -1689,18 +1682,20 @@ class KVCacheConfigurator:
 
         configurator = create_memory_pool_configurator(self)
         config = configurator.calculate_pool_sizes(
-            budget_bytes, self.server_args.page_size
+            budget_bytes, get_schedule().page_size
         )
         max_tokens = self._apply_token_constraints(config.max_total_num_tokens)
         if cap_tokens is not None:
             max_tokens = min(max_tokens, cap_tokens)
         if max_tokens != config.max_total_num_tokens:
             config = configurator.calculate_pool_sizes_from_max_tokens(
-                max_tokens, self.server_args.page_size
+                max_tokens, get_schedule().page_size
             )
         return config
 
     def _handle_max_mamba_cache(self, total_rest_memory):
+        from sglang.srt.runtime_context import get_context
+
         config = self.mambaish_config
         server_args = self.server_args
         assert config is not None
@@ -1710,11 +1705,11 @@ class KVCacheConfigurator:
             assert server_args.speculative_num_draft_tokens is not None
             assert server_args.max_running_requests is not None
 
-        if server_args.max_mamba_cache_size is not None:
+        if get_schedule().max_mamba_cache_size is not None:
             # Use explicitly set max_mamba_cache_size
-            server_args.override(
+            get_context().override(
                 "mamba_pool.per_dp_shard",
-                max_mamba_cache_size=server_args.max_mamba_cache_size
+                max_mamba_cache_size=get_schedule().max_mamba_cache_size
                 // self.ps.attn_dp_size,
             )
             # Reserve intermediate memory based on capped max_num_reqs
@@ -1722,7 +1717,7 @@ class KVCacheConfigurator:
                 ratio = self._calculate_mamba_ratio()
                 capped_reqs = min(
                     server_args.max_running_requests // self.ps.attn_dp_size,
-                    server_args.max_mamba_cache_size // ratio,
+                    get_schedule().max_mamba_cache_size // ratio,
                 )
                 intermediate_size = (
                     config.mamba2_cache_params.mamba_cache_per_req
@@ -1735,7 +1730,7 @@ class KVCacheConfigurator:
             and server_args.max_running_requests is not None
         ):
             # Use explicitly set max_running_requests when radix cache is disabled
-            server_args.override(
+            get_context().override(
                 "mamba_pool.from_max_running_requests",
                 max_mamba_cache_size=server_args.max_running_requests
                 // self.ps.attn_dp_size,
@@ -1744,7 +1739,7 @@ class KVCacheConfigurator:
             if has_spec_dec:
                 intermediate_size = (
                     config.mamba2_cache_params.mamba_cache_per_req
-                    * server_args.max_mamba_cache_size
+                    * get_schedule().max_mamba_cache_size
                     * server_args.speculative_num_draft_tokens
                 )
                 total_rest_memory = total_rest_memory - (intermediate_size / (1 << 30))
@@ -1769,7 +1764,7 @@ class KVCacheConfigurator:
                 ratio = self._calculate_mamba_ratio()
                 D = server_args.speculative_num_draft_tokens
                 # Joint solve: main_state + intermediate = mamba_budget
-                server_args.override(
+                get_context().override(
                     "mamba_pool.memory_budget_spec",
                     max_mamba_cache_size=int(
                         mamba_budget_bytes // (per_req * (1 + D / ratio))
@@ -1779,12 +1774,12 @@ class KVCacheConfigurator:
                 # so the return value only has main_state subtracted from total
                 capped_reqs = min(
                     server_args.max_running_requests // self.ps.attn_dp_size,
-                    server_args.max_mamba_cache_size // ratio,
+                    get_schedule().max_mamba_cache_size // ratio,
                 )
                 intermediate_size = per_req * capped_reqs * D
                 total_rest_memory = total_rest_memory - (intermediate_size / (1 << 30))
             else:
-                server_args.override(
+                get_context().override(
                     "mamba_pool.memory_budget",
                     max_mamba_cache_size=int(mamba_budget_bytes // per_req),
                 )
@@ -1793,10 +1788,10 @@ class KVCacheConfigurator:
         # A non-positive value means GPU memory is insufficient for the requested
         # configuration. Fail fast with actionable advice instead of silently
         # producing garbled output at runtime.
-        if server_args.max_mamba_cache_size <= 0:
+        if get_schedule().max_mamba_cache_size <= 0:
             raise RuntimeError(
                 f"Not enough GPU memory for hybrid (mamba/linear-attention) state cache. "
-                f"Computed max_mamba_cache_size={server_args.max_mamba_cache_size} "
+                f"Computed max_mamba_cache_size={get_schedule().max_mamba_cache_size} "
                 f"(total_rest_memory={total_rest_memory:.2f} GB, "
                 f"mamba_cache_per_req={config.mamba2_cache_params.mamba_cache_per_req / (1 << 20):.2f} MB). "
                 f"Try: (1) reduce --max-running-requests, "
@@ -1806,7 +1801,7 @@ class KVCacheConfigurator:
             )
 
         mamba_state_memory = (
-            server_args.max_mamba_cache_size
+            get_schedule().max_mamba_cache_size
             * config.mamba2_cache_params.mamba_cache_per_req
             / (1 << 30)
         )

python/sglang/srt/mem_cache/storage/lmcache/lmc_radix_cache.py

@@ -16,7 +16,7 @@ from sglang.srt.mem_cache.base_prefix_cache import (
     MatchResult,
 )
 from sglang.srt.mem_cache.radix_cache import RadixCache, RadixKey, TreeNode
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_memory, get_server_args
 
 try:
     from lmcache.integration.sglang.multi_process_adapter import LMCacheMPConnector
@@ -108,7 +108,7 @@ class LMCRadixCache(RadixCache):
     ):
         super().__init__(params)
 
-        cli_lmc_cfg = get_server_args().lmcache_config_file or ""
+        cli_lmc_cfg = get_memory().lmcache_config_file or ""
 
         kvcache = self.token_to_kv_pool_allocator.get_kvcache()
         connector_kwargs = dict(

python/sglang/srt/model_executor/forward_batch_info.py

@@ -51,13 +51,8 @@ from sglang.srt.layers.dp_attention import (
 from sglang.srt.model_executor.forward_batch_deepseek_mha_mixin import (
     ForwardBatchDeepSeekMHAMixin,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
-from sglang.srt.utils import (
-    is_cuda,
-    is_hip,
-    is_npu,
-    support_triton,
-)
+from sglang.srt.runtime_context import get_exec, get_parallel
+from sglang.srt.utils import is_cuda, is_hip, is_npu, support_triton
 from sglang.srt.utils.common import ceil_align, is_pin_memory_available
 
 if TYPE_CHECKING:
@@ -941,7 +936,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
             # --enable-mis: every request must carry delimiter indices (the score
             # endpoint always produces MIS-structured requests; consumers index
             # without None-checking).
-            if get_server_args().enable_mis and any(
+            if get_exec().features.enable_mis and any(
                 r.multi_item_delimiter_indices is not None for r in batch.reqs
             ):
                 assert all(
@@ -1110,7 +1105,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
         # batch_size * [3 * seq_len]
         batch_size = self.seq_lens_cpu.shape[0]
         mrope_positions_list = [[]] * batch_size
-        rl_on_policy_target = get_server_args().rl_on_policy_target
+        rl_on_policy_target = get_exec().deterministic.rl_on_policy_target
         for batch_idx in range(batch_size):
             mm_input = batch.multimodal_inputs[batch_idx]
             if self.forward_mode.is_decode():

python/sglang/srt/model_executor/model_runner.py

@@ -26,11 +26,7 @@ import torch
 import torch.distributed as dist
 
 from sglang.srt.configs.load_config import LoadConfig
-from sglang.srt.configs.model_config import (
-    AttentionArch,
-    ModelConfig,
-    ModelImpl,
-)
+from sglang.srt.configs.model_config import AttentionArch, ModelConfig, ModelImpl
 from sglang.srt.configs.update_config import adjust_config_with_unaligned_cpu_tp
 from sglang.srt.debug_utils.dumper import dumper
 from sglang.srt.distributed import bootstrap
@@ -74,9 +70,7 @@ from sglang.srt.kv_canary.runner.canary_manager import context_tuple
 from sglang.srt.kv_canary.token_oracle.install import install_token_oracle_from_env
 from sglang.srt.layers import deep_gemm_wrapper, model_parallel
 from sglang.srt.layers.attention.dsa.utils import is_dsa_enable_prefill_cp
-from sglang.srt.layers.cp.utils import (
-    get_cp_strategy,
-)
+from sglang.srt.layers.cp.utils import get_cp_strategy
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import create_sampler
 from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
@@ -86,17 +80,10 @@ from sglang.srt.lora.lora_registry import LoRARef
 from sglang.srt.managers.schedule_batch import sanity_check_mm_pad_shift_value
 from sglang.srt.mem_cache import kv_cache_dtype
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
-from sglang.srt.mem_cache.kv_cache_configurator import (
-    KVCacheConfigurator,
-)
+from sglang.srt.mem_cache.kv_cache_configurator import KVCacheConfigurator
 from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
-from sglang.srt.model_executor.cuda_graph_config import (
-    cuda_graph_fully_disabled,
-)
-from sglang.srt.model_executor.forward_batch_info import (
-    ForwardBatch,
-    PPProxyTensors,
-)
+from sglang.srt.model_executor.cuda_graph_config import cuda_graph_fully_disabled
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_executor.forward_context import (
     ForwardContext,
     forward_context,
@@ -155,14 +142,15 @@ from sglang.srt.model_executor.model_runner_components.weight_updater import (
     WeightUpdater,
 )
 from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
-from sglang.srt.model_executor.runner import (
-    EagerRunner,
-    get_batch_sizes_to_capture,
-)
+from sglang.srt.model_executor.runner import EagerRunner, get_batch_sizes_to_capture
 from sglang.srt.platforms import current_platform
 from sglang.srt.runtime_context import (
+    get_device,
+    get_exec,
     get_global_dwdp_manager,
-    get_server_args,
+    get_lora,
+    get_model,
+    get_schedule,
     set_global_dwdp_manager,
 )
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
@@ -319,7 +307,7 @@ class ModelRunner:
             self.init_threads_binding()
 
         # Set float32 matmul precision
-        if get_server_args().enable_tf32_matmul:
+        if get_exec().features.enable_tf32_matmul:
             torch.set_float32_matmul_precision("high")
 
         # Set device early so that TransferEngine init (e.g. Ascend NPU)
@@ -396,12 +384,12 @@ class ModelRunner:
 
     def _initialize_elastic_ep_joiner(self) -> None:
         if not (
-            self.server_args.elastic_ep_backend is not None
+            get_exec().moe.elastic_ep_backend is not None
             and self.server_args.is_ep_joiner
         ):
             return
 
-        is_scale_join = self.server_args.ep_join_mode == "scale"
+        is_scale_join = get_exec().moe.ep_join_mode == "scale"
         if is_scale_join:
             join_effective_ep_size = (
                 self.server_args.ep_join_rank_offset + self.ps.tp_size
@@ -484,7 +472,7 @@ class ModelRunner:
             device=self.device,
             gpu_id=self.gpu_id,
             model_config=self.model_config,
-            custom_weight_loaders=self.server_args.custom_weight_loader,
+            custom_weight_loaders=get_model().custom_weight_loader,
             get_model=lambda: self.model,
             update_model_fields=self.update_model_fields,
             recapture_cuda_graph=self.init_decode_cuda_graph,
@@ -561,7 +549,7 @@ class ModelRunner:
     def init_mindspore_runner(self):
         # Init the mindspore runner
         # for now, there is only some communication initialization work
-        if self.server_args.model_impl.lower() == ModelImpl.MINDSPORE and _is_npu:
+        if get_model().model_impl.lower() == ModelImpl.MINDSPORE and _is_npu:
             from sglang.srt.model_executor.mindspore_runner import init_ms_distributed
 
             init_ms_distributed(
@@ -618,7 +606,7 @@ class ModelRunner:
 
     def init_memory_saver_adapter(self):
         self.memory_saver_adapter = TorchMemorySaverAdapter.create(
-            enable=self.server_args.enable_memory_saver
+            enable=get_exec().features.enable_memory_saver
         )
 
     def maybe_init_remote_instance_transfer_engine(self):
@@ -654,7 +642,7 @@ class ModelRunner:
         )
 
     def maybe_init_lplb_solvers(self):
-        if self.server_args.ep_dispatch_algorithm == "lp" and not self.is_draft_worker:
+        if get_exec().moe.ep_dispatch_algorithm == "lp" and not self.is_draft_worker:
             init_lplb_solvers(model_config=self.model_config)
 
     def maybe_init_eplb_manager(self):
@@ -668,12 +656,12 @@ class ModelRunner:
                 get_expert_backup_client=lambda: self.expert_backup_client,
                 get_weight_updater=lambda: self.weight_updater,
             )
-            if self.server_args.enable_eplb and (not self.is_draft_worker)
+            if get_exec().moe.enable_eplb and (not self.is_draft_worker)
             else None
         )
 
     def maybe_init_elastic_ep(self):
-        if self.server_args.elastic_ep_backend:
+        if get_exec().moe.elastic_ep_backend:
             ElasticEPStateManager.init(self.server_args)
 
     def init_token_oracle(self):
@@ -692,8 +680,8 @@ class ModelRunner:
                 get_model=lambda: self.model,
             )
             if (
-                self.server_args.enable_elastic_expert_backup
-                and self.server_args.elastic_ep_backend is not None
+                get_exec().moe.enable_elastic_expert_backup
+                and get_exec().moe.elastic_ep_backend is not None
             )
             else None
         )
@@ -702,17 +690,17 @@ class ModelRunner:
         # In layered loading, torchao may have been applied
         torchao_applied = getattr(self.model, "torchao_applied", False)
         if not torchao_applied:
-            apply_torchao_config_to_model(self.model, get_server_args().torchao_config)
+            apply_torchao_config_to_model(self.model, get_exec().graph.torchao_config)
         supports_torch_tp = getattr(self.model, "supports_torch_tp", False)
         if self.ps.tp_size > 1 and supports_torch_tp:
             self.apply_torch_tp()
 
     def maybe_init_lora_manager(self):
-        if self.server_args.enable_lora:
+        if get_lora().enable_lora:
             self.init_lora_manager()
 
     def maybe_enable_batch_invariant_mode(self):
-        if self.server_args.enable_deterministic_inference:
+        if get_exec().deterministic.enable_deterministic_inference:
             from sglang.srt.batch_invariant_ops import enable_batch_invariant_mode
 
             enable_batch_invariant_mode()
@@ -973,7 +961,7 @@ class ModelRunner:
             get_offloader().post_init()
 
         # Register model for layerwise NVTX profiling if enabled
-        if self.server_args.enable_layerwise_nvtx_marker:
+        if get_exec().comm.enable_layerwise_nvtx_marker:
             pyt_hooks = PytHooks()
             pyt_hooks.register_hooks(self.model, module_prefix="model")
 
@@ -1030,7 +1018,7 @@ class ModelRunner:
         )
 
         dist_barrier_after_load(
-            elastic_ep_backend=self.server_args.elastic_ep_backend,
+            elastic_ep_backend=get_exec().moe.elastic_ep_backend,
             tp_rank=self.ps.tp_rank,
             is_ep_scale_joiner=self.server_args.is_ep_scale_joiner,
         )
@@ -1050,16 +1038,16 @@ class ModelRunner:
         self.lora_manager = LoRAManager(
             base_model=self.model,
             base_hf_config=self.model_config.hf_config,
-            max_loras_per_batch=self.server_args.max_loras_per_batch,
+            max_loras_per_batch=get_lora().max_loras_per_batch,
             load_config=self.load_config,
             dtype=self.dtype,
             server_args=self.server_args,
-            lora_backend=self.server_args.lora_backend,
+            lora_backend=get_lora().lora_backend,
             tp_size=self.ps.tp_size,
             tp_rank=self.ps.tp_rank,
-            max_lora_rank=self.server_args.max_lora_rank,
-            target_modules=self.server_args.lora_target_modules,
-            lora_paths=self.server_args.lora_paths,
+            max_lora_rank=get_lora().max_lora_rank,
+            target_modules=get_lora().lora_target_modules,
+            lora_paths=get_lora().lora_paths,
         )
         if not cuda_graph_fully_disabled():
             init_lora_cuda_graph_moe_buffers(
@@ -1331,7 +1319,7 @@ class ModelRunner:
                 )
         output.expert_distribution_metrics = recorder_outputs.get("metrics")
 
-        no_copy_to_cpu = not self.server_args.disable_overlap_schedule
+        no_copy_to_cpu = not get_schedule().disable_overlap_schedule
         if (
             not self.is_draft_worker
             and (experts_capturer := get_global_experts_capturer()) is not None
@@ -1361,7 +1349,7 @@ class ModelRunner:
             self.msprobe_debugger.stop()
             self.msprobe_debugger.step()
 
-        if self.server_args.elastic_ep_backend is not None:
+        if get_exec().moe.elastic_ep_backend is not None:
             self.maybe_join_ep_ranks()
 
         return output
@@ -1765,7 +1753,7 @@ class ModelRunner:
             recovered = maybe_recover_ep_ranks(
                 tp_group=self.tp_group,
                 eplb_manager=self.eplb_manager,
-                random_seed=self.server_args.random_seed,
+                random_seed=get_device().random_seed,
             )
             if recovered:
                 self.forward_pass_id = 0
@@ -1774,7 +1762,7 @@ class ModelRunner:
         local_timeout = (
             state.pending_since is not None
             and time.monotonic() - state.pending_since
-            > self.server_args.elastic_ep_scale_timeout
+            > get_exec().moe.elastic_ep_scale_timeout
         )
         timeout = state.active_ranks.new_tensor(int(local_timeout))
         dist.all_reduce(timeout, op=dist.ReduceOp.MAX, group=dist.group.WORLD)
@@ -1842,7 +1830,9 @@ class ModelRunner:
         load_config: LoadConfig,
     ) -> None:
         self.model = new_model
-        self.server_args.override(
+        from sglang.srt.runtime_context import get_context
+
+        get_context().override(
             "model_runner.update_model_fields",
             model_path=model_path,
             load_format=load_format,

python/sglang/srt/model_executor/model_runner_components/misc_utils.py

@@ -24,6 +24,12 @@ def maybe_disable_chunked_prefix_cache(
     # model's (often non-MLA) config must not flip the shared setting.
     if is_draft_worker:
         return
+
+    # This is a load-time gate that runs in ModelRunner.__init__ BEFORE the
+    # runner publishes its config (and direct/benchmark construction never
+    # publishes earlier), so read/write the supplied server_args. The runner's
+    # subsequent publish snapshots this into the schedule bag for get_schedule()
+    # readers.
     if (
         not use_mla_backend
         or server_args.attention_backend

python/sglang/srt/model_executor/model_runner_components/remote_instance_weight_transporter.py

@@ -11,6 +11,7 @@ from sglang.srt.model_loader.remote_instance_weight_loader_utils import (
     RemoteInstanceWeightLoaderBackend,
     register_memory_region,
 )
+from sglang.srt.runtime_context import get_model
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.network import NetworkAddress, get_local_ip_auto
 
@@ -58,7 +59,7 @@ class RemoteInstanceWeightTransporter:
             # ModelExpress owns TransferEngine memory registration and metadata
             # publishing for backend=modelexpress. Re-registering here would
             # overlap the same weight buffers.
-            and self.server_args.remote_instance_weight_loader_backend
+            and get_model().remote_instance_weight_loader_backend
             != RemoteInstanceWeightLoaderBackend.MODELEXPRESS
             and self.engine is not None
             and self.weight_info is None
@@ -84,7 +85,7 @@ class RemoteInstanceWeightTransporter:
         else:
             bootstrap_host = "127.0.0.1"
 
-        bootstrap_port = self.server_args.engine_info_bootstrap_port
+        bootstrap_port = get_model().engine_info_bootstrap_port
         bootstrap_na = NetworkAddress(bootstrap_host, bootstrap_port)
         url = f"{bootstrap_na.to_url()}/register_transfer_engine_info"
 

python/sglang/srt/model_loader/loader.py

@@ -44,7 +44,7 @@ from sglang.srt.model_loader.remote_instance_weight_loader_utils import (
     get_remote_instance_transfer_engine_info_per_rank,
     register_memory_region,
 )
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec, get_server_args
 from sglang.srt.utils import get_available_gpu_memory
 
 # Try to import accelerate (optional dependency)
@@ -71,9 +71,7 @@ from sglang.srt.connector import (
     get_connector_type,
 )
 from sglang.srt.connector.utils import parse_model_name
-from sglang.srt.distributed import (
-    model_parallel_is_initialized,
-)
+from sglang.srt.distributed import model_parallel_is_initialized
 from sglang.srt.layers.modelopt_utils import QUANT_CFG_CHOICES
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.model_loader.remote_instance_weight_loader_utils import (
@@ -865,9 +863,8 @@ class LayeredModelLoader(DefaultModelLoader):
         device_config: DeviceConfig,
     ) -> nn.Module:
         from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
-        from sglang.srt.runtime_context import get_server_args
 
-        torchao_config = get_server_args().torchao_config
+        torchao_config = get_exec().graph.torchao_config
         target_device = torch.device(device_config.device)
         quant_config = _get_quantization_config(model_config, self.load_config)
 

python/sglang/srt/models/bailing_moe.py

@@ -41,9 +41,7 @@ from sglang.srt.layers.communicator import (
     LayerScatterModes,
     enable_moe_dense_fully_dp,
 )
-from sglang.srt.layers.dp_attention import (
-    is_dp_attention_enabled,
-)
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     MergedColumnParallelLinear,
@@ -78,6 +76,7 @@ from sglang.srt.models.utils import (
     enable_fused_set_kv_buffer,
 )
 from sglang.srt.runtime_context import (
+    get_exec,
     get_forward,
     get_parallel,
     get_server_args,
@@ -209,7 +208,7 @@ class BailingMoESparseMoeBlock(nn.Module):
             self.router_dtype = torch.bfloat16
 
         # TODO global_server_args.ep_num_redundant_experts is used for eplb, not supported now
-        assert get_server_args().ep_num_redundant_experts == 0
+        assert get_exec().moe.ep_num_redundant_experts == 0
         # check group topk
         self.num_expert_group = getattr(config, "n_group", 0)
         self.topk_group = getattr(config, "topk_group", 0)
@@ -223,9 +222,7 @@ class BailingMoESparseMoeBlock(nn.Module):
             self.num_expert_group = self.topk_group = None
             self.use_grouped_topk = False
 
-        self.num_experts = (
-            config.num_experts + get_server_args().ep_num_redundant_experts
-        )
+        self.num_experts = config.num_experts + get_exec().moe.ep_num_redundant_experts
 
         self.gate = BailingMoEGate(
             config=config,

python/sglang/srt/models/bailing_moe_linear.py

@@ -12,17 +12,12 @@ from transformers import PretrainedConfig
 from sglang.kernels.ops.attention.fla.layernorm_gated import RMSNorm as RMSNormGated
 from sglang.kernels.ops.attention.fla.layernorm_gated import layernorm_fn
 from sglang.kernels.ops.quantization.fp8_kernel import is_fp8_fnuz
-from sglang.srt.distributed import (
-    get_pp_group,
-    tensor_model_parallel_all_reduce,
-)
+from sglang.srt.distributed import get_pp_group, tensor_model_parallel_all_reduce
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
-from sglang.srt.layers.dp_attention import (
-    is_dp_attention_enabled,
-)
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,
@@ -59,6 +54,7 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA, DeepseekV2MLP, _is_hip
 from sglang.srt.models.utils import WeightsMapper
 from sglang.srt.runtime_context import (
+    get_device,
     get_forward,
     get_parallel,
     get_server_args,
@@ -529,7 +525,7 @@ class BailingMoELinearAttention(nn.Module):
             base=self.rope_theta,
             rope_scaling=config.rope_scaling,
             is_neox_style=True,
-            device=get_server_args().device,
+            device=get_device().device,
             dtype=torch.float32,
         )
 
@@ -690,7 +686,7 @@ class BailingMoEAttention(nn.Module):
             max_position=self.max_position_embeddings,
             base=self.rope_theta,
             rope_scaling=config.rope_scaling,
-            device=get_server_args().device,
+            device=get_device().device,
         )
         self.attn = RadixAttention(
             self.num_heads,

python/sglang/srt/models/bert.py

@@ -16,7 +16,7 @@ from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_model, get_parallel
 from sglang.srt.utils import add_prefix
 
 BertConfig = None
@@ -365,9 +365,7 @@ class BertModel(nn.Module):
             quant_config=quant_config,
             prefix=add_prefix("encoder", prefix),
         )
-        pooling_type = (
-            PoolingType.CLS if get_server_args().is_embedding else PoolingType.LAST
-        )
+        pooling_type = PoolingType.CLS if get_model().is_embedding else PoolingType.LAST
         self.pooler = (
             BertPooler(config)
             if self.use_bert_pooler

python/sglang/srt/models/deepseek_common/attention_backend_handler.py

@@ -11,7 +11,7 @@ from sglang.srt.models.deepseek_common.attention_forward_methods.forward_methods
     AttnForwardMethod,
 )
 from sglang.srt.models.deepseek_common.utils import _is_hip
-from sglang.srt.runtime_context import get_server_args
+from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import use_intel_amx_backend
 
 MHA_ONE_SHOT_SUPPORTED_BACKENDS = ["fa3", "flashinfer", "flashmla"]
@@ -118,7 +118,7 @@ def handle_attention_flashinfer(attn, forward_batch):
 
 def handle_attention_fa3(attn, forward_batch):
     # when deterministic inference is enabled, use MLA
-    if get_server_args().enable_deterministic_inference:
+    if get_exec().deterministic.enable_deterministic_inference:
         return _dispatch_mla_subtype(attn, forward_batch)
     else:
         return _handle_attention_backend(attn, forward_batch, "fa3")
@@ -187,7 +187,7 @@ def handle_attention_triton(attn, forward_batch):
         return AttnForwardMethod.MLA
 
     # when deterministic inference is enabled, use MLA
-    if get_server_args().enable_deterministic_inference:
+    if get_exec().deterministic.enable_deterministic_inference:
         return _dispatch_mla_subtype(attn, forward_batch)
 
     if (

python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py

@@ -30,7 +30,11 @@ from sglang.srt.models.deepseek_common.utils import (
     _use_aiter_bpreshuffle_gfx95,
     _use_aiter_gfx95,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_exec,
+    get_parallel,
+    get_schedule,
+)
 from sglang.srt.utils import BumpAllocator, get_bool_env_var, next_power_of_2
 
 _use_fp8_prefill_attn = (
@@ -142,9 +146,7 @@ def _forward_dsa_indexer_for_mha(
 
 class DeepseekMHAForwardMixin:
     def init_mha_forward(self: DeepseekV2AttentionMLA):
-        self.disable_chunked_prefix_cache = (
-            get_server_args().disable_chunked_prefix_cache
-        )
+        self.disable_chunked_prefix_cache = get_schedule().disable_chunked_prefix_cache
 
         # TODO: Design a finer way to determine the threshold
         self.chunked_prefix_cache_threshold = (
@@ -305,8 +307,8 @@ class DeepseekMHAForwardMixin:
                 self.use_dsa
                 and self.kv_cache_dtype == "fp8_e4m3"
                 and (
-                    not get_server_args().dsa_decode_backend == "trtllm"
-                    or not get_server_args().dsa_prefill_backend == "trtllm"
+                    not get_exec().kernel.dsa_decode_backend == "trtllm"
+                    or not get_exec().kernel.dsa_prefill_backend == "trtllm"
                 )
             ):
                 # FP8 path: dequantize DSA-specific FP8 format to BF16

python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mla.py

@@ -65,10 +65,8 @@ from sglang.srt.models.deepseek_common.utils import (
     _use_aiter_bpreshuffle_gfx95,
     _use_aiter_gfx95,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
-from sglang.srt.state_capturer.indexer_topk import (
-    maybe_capture_indexer_topk,
-)
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args
+from sglang.srt.state_capturer.indexer_topk import maybe_capture_indexer_topk
 from sglang.srt.utils import BumpAllocator
 from sglang.srt.utils.custom_op import register_custom_op
 
@@ -153,7 +151,7 @@ def _should_defer_dsa_cp_kv_gather(
 class DeepseekMLAForwardMixin:
     def init_mla_forward(self: DeepseekV2AttentionMLA):
         self.flashinfer_mla_disable_ragged = (
-            get_server_args().flashinfer_mla_disable_ragged
+            get_exec().kernel.flashinfer_mla_disable_ragged
         )
 
     def should_run_indexer(
@@ -990,8 +988,8 @@ class DeepseekMLAForwardMixin:
         """
         if self.current_attention_backend in ("dsa", "nsa"):
             return (
-                get_server_args().dsa_decode_backend == "trtllm"
-                or get_server_args().dsa_prefill_backend == "trtllm"
+                get_exec().kernel.dsa_decode_backend == "trtllm"
+                or get_exec().kernel.dsa_prefill_backend == "trtllm"
             ) and get_attn_backend().kv_cache_dtype == torch.float8_e4m3fn
 
         return (

python/sglang/srt/models/deepseek_nextn.py

@@ -59,7 +59,12 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.models.deepseek_common.utils import enable_nextn_moe_bf16_cast_to_fp8
 from sglang.srt.models.deepseek_v2 import DeepseekV2DecoderLayer, DeepseekV3ForCausalLM
 from sglang.srt.models.utils import WeightsMapper
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_model,
+    get_parallel,
+    get_server_args,
+    get_spec,
+)
 from sglang.srt.utils import BumpAllocator, add_prefix, is_cuda, is_npu
 
 
@@ -148,7 +153,7 @@ class DeepseekModelNextN(nn.Module):
 
         self.rot_weight = None
         if _is_npu:
-            rot_weight_path = get_server_args().model_path + "/rot.safetensors"
+            rot_weight_path = get_model().model_path + "/rot.safetensors"
             if os.path.isfile(rot_weight_path):
                 self.rot_weight = load_file(rot_weight_path)
                 self.rot_weight = self.rot_weight["rot.weight"].npu()
@@ -161,8 +166,7 @@ class DeepseekModelNextN(nn.Module):
 
         layer_name = "decoder"
         if _is_npu and (
-            get_server_args().speculative_draft_model_path
-            == get_server_args().model_path
+            get_spec().speculative_draft_model_path == get_model().model_path
         ):
             layer_name = "layers." + str(config.num_hidden_layers)
 
@@ -201,7 +205,7 @@ class DeepseekModelNextN(nn.Module):
         if (
             _is_npu
             and self.quant_config is None
-            and get_server_args().quantization is not None
+            and get_model().quantization is not None
         ):
             # ascend mtp unquant
             exit_stack.enter_context(envs.SGLANG_DEEPEP_BF16_DISPATCH.override(True))

python/sglang/srt/models/deepseek_v2.py

@@ -29,10 +29,7 @@ import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
 
-from sglang.jit_kernel.dsv4 import (
-    silu_and_mul_clamp,
-    silu_and_mul_contig_post_quant,
-)
+from sglang.jit_kernel.dsv4 import silu_and_mul_clamp, silu_and_mul_contig_post_quant
 from sglang.kernels.ops.quantization.fp8_kernel import (
     create_per_token_group_quant_fp8_output_scale,
 )
@@ -78,9 +75,7 @@ from sglang.srt.layers.communicator_dsa_cp import (
     maybe_prefetch_next_full_attention_kv,
 )
 from sglang.srt.layers.cp.utils import is_cp_v2_active
-from sglang.srt.layers.dcp.planner import (
-    prepare_decode_context_parallel_metadata,
-)
+from sglang.srt.layers.dcp.planner import prepare_decode_context_parallel_metadata
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,
@@ -115,9 +110,7 @@ from sglang.srt.layers.moe.utils import (
 )
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8 import Fp8Config
-from sglang.srt.layers.quantization.fp8_utils import (
-    materialize_bpreshuffle_fp8_scale,
-)
+from sglang.srt.layers.quantization.fp8_utils import materialize_bpreshuffle_fp8_scale
 from sglang.srt.layers.quantization.mxfp4_flashinfer_trtllm_moe import (
     maybe_fuse_routed_scale_and_shared_add,
 )
@@ -183,11 +176,14 @@ from sglang.srt.models.deepseek_common.utils import (
     is_wint4afp8_or_wint4a16_config,
 )
 from sglang.srt.runtime_context import (
+    get_device,
+    get_exec,
     get_flags,
     get_forward,
     get_model,
     get_parallel,
     get_server_args,
+    get_spec,
 )
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
@@ -381,9 +377,7 @@ class DeepseekV2MLP(nn.Module):
             return down_output
 
         if self.use_fused_clamp_act_mul and self.swiglu_limit is not None:
-            from aiter.ops.triton.fusions.fused_clamp_act_mul import (
-                fused_clamp_act_mul,
-            )
+            from aiter.ops.triton.fusions.fused_clamp_act_mul import fused_clamp_act_mul
 
             if not self._fused_clamp_fp8_checked:
                 from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
@@ -494,7 +488,7 @@ class MoEGate(nn.Module):
                 True,  # is_vnni
             )
 
-        if get_server_args().enable_deterministic_inference:
+        if get_exec().deterministic.enable_deterministic_inference:
             return F.linear(hidden_states, self.weight, None)
 
         if (
@@ -560,7 +554,7 @@ class DeepseekV2MoE(nn.Module):
         n_shared_experts = (
             0 if config.n_shared_experts is None else int(config.n_shared_experts)
         )
-        _fusion_disabled = get_server_args().disable_shared_experts_fusion
+        _fusion_disabled = get_exec().moe.disable_shared_experts_fusion
 
         # num_fused_shared_experts drives weight remapping in deepseek_weight_loader:
         # mlp.shared_experts → mlp.experts.256 when > 0.
@@ -630,8 +624,7 @@ class DeepseekV2MoE(nn.Module):
             fused_shared_experts_scaling_factor = 1.0 / float(self.moe_ep_size)
 
         self.experts = get_moe_impl_class(quant_config)(
-            num_experts=num_experts_for_moe
-            + get_server_args().ep_num_redundant_experts,
+            num_experts=num_experts_for_moe + get_exec().moe.ep_num_redundant_experts,
             num_fused_shared_experts=self.num_fused_shared_experts,
             top_k=top_k_for_moe,
             hidden_size=config.hidden_size,
@@ -804,7 +797,7 @@ class DeepseekV2MoE(nn.Module):
             # TODO: we will support tp < ep in the future
             self.ep_size = get_parallel().moe_ep_size
             self.num_experts = (
-                config.n_routed_experts + get_server_args().ep_num_redundant_experts
+                config.n_routed_experts + get_exec().moe.ep_num_redundant_experts
             )
             self.renormalize = config.norm_topk_prob
             self.topk_group = config.topk_group
@@ -1718,7 +1711,7 @@ class DeepseekV2AttentionMLA(
                 base=rope_theta,
                 rope_scaling=rope_scaling,
                 is_neox_style=is_neox_style,
-                device=get_server_args().device,
+                device=get_device().device,
             )
 
             if rope_scaling and rope_scaling.get("apply_yarn_scaling", True):
@@ -2069,7 +2062,7 @@ class DeepseekV2DecoderLayer(nn.Module):
             rope_scaling = config.rope_scaling
         max_position_embeddings = config.max_position_embeddings
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
-            get_server_args().speculative_algorithm
+            get_spec().speculative_algorithm
         )
         self.dsa_enable_prefill_cp = dsa_enable_prefill_cp
         self.mla_enable_prefill_cp = mla_enable_prefill_cp
@@ -2765,7 +2758,7 @@ class DeepseekV2ForCausalLM(nn.Module, DeepseekV2WeightLoaderMixin):
         self.num_fused_shared_experts = 0
         server_args = get_server_args()
 
-        if get_server_args().disable_shared_experts_fusion:
+        if get_exec().moe.disable_shared_experts_fusion:
             return
 
         disable_reason = None

python/sglang/srt/models/deepseek_v4.py

@@ -29,18 +29,11 @@ from sglang.jit_kernel.dsv4 import (
     fused_rope_inplace,
     sglang_per_token_group_quant_fp8_dsv4_wo_a,
 )
-from sglang.kernels.ops.attention.deepseek_v4_rope import (
-    v4_rope_inplace_npu,
-)
-from sglang.kernels.ops.quantization.fp8_kernel import (
-    sglang_per_token_group_quant_fp8,
-)
+from sglang.kernels.ops.attention.deepseek_v4_rope import v4_rope_inplace_npu
+from sglang.kernels.ops.quantization.fp8_kernel import sglang_per_token_group_quant_fp8
 from sglang.srt.compilation.compilation_config import register_split_op
 from sglang.srt.configs.deepseek_v4 import DeepSeekV4Config
-from sglang.srt.distributed import (
-    get_pp_group,
-    get_tp_group,
-)
+from sglang.srt.distributed import get_pp_group, get_tp_group
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
@@ -136,7 +129,13 @@ from sglang.srt.models.deepseek_v2 import (
     _is_npu,
     _is_xpu,
 )
-from sglang.srt.runtime_context import get_forward, get_parallel, get_server_args
+from sglang.srt.runtime_context import (
+    get_device,
+    get_exec,
+    get_forward,
+    get_parallel,
+    get_server_args,
+)
 
 if not _is_hip:
     from sglang.srt.layers.utils.cp_utils import (
@@ -311,9 +310,7 @@ def _freqs_cis_to_cos_sin(
 
 
 if TYPE_CHECKING:
-    from sglang.srt.layers.attention.deepseek_v4_backend import (
-        DeepseekV4AttnBackend,
-    )
+    from sglang.srt.layers.attention.deepseek_v4_backend import DeepseekV4AttnBackend
     from sglang.srt.layers.attention.deepseek_v4_backend_hip_radix import (
         DeepseekV4HipRadixBackend,
     )
@@ -575,7 +572,7 @@ class MQALayer(MqaAttentionBase):
             base=self.rope_base,
             rope_scaling=self.rope_scaling,
             is_neox_style=False,
-            device=get_server_args().device,
+            device=get_device().device,
         )
 
         if _is_hip:
@@ -2458,11 +2455,11 @@ class DeepseekV4ForCausalLM(nn.Module):
 
     def determine_num_fused_shared_experts(self):
         self.num_fused_shared_experts = 0
-        if get_server_args().disable_shared_experts_fusion:
+        if get_exec().moe.disable_shared_experts_fusion:
             return
 
         disable_reason = None
-        if get_server_args().enforce_shared_experts_fusion:
+        if get_exec().moe.enforce_shared_experts_fusion:
             if self.config.n_shared_experts != 1:
                 raise ValueError(
                     "DeepSeek V4 shared-experts fusion expects exactly one shared "

python/sglang/srt/models/exaone_moe.py

@@ -24,17 +24,12 @@ import torch
 from torch import nn
 from transformers import PretrainedConfig
 
-from sglang.srt.distributed import (
-    get_pp_group,
-    tensor_model_parallel_all_reduce,
-)
+from sglang.srt.distributed import get_pp_group, tensor_model_parallel_all_reduce
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
 from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
 from sglang.srt.layers.activation import SiluAndMul
-from sglang.srt.layers.dp_attention import (
-    is_dp_attention_enabled,
-)
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     MergedColumnParallelLinear,
@@ -62,7 +57,12 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_executor.runner import get_is_capture_mode
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.runtime_context import get_parallel, get_server_args, get_stream
+from sglang.srt.runtime_context import (
+    get_exec,
+    get_parallel,
+    get_server_args,
+    get_stream,
+)
 from sglang.srt.utils import LazyValue, add_prefix, is_cuda, make_layers
 
 logger = logging.getLogger(__name__)
@@ -165,7 +165,7 @@ class ExaoneMoESparseMoEBlock(nn.Module):
         )
 
         self.experts = get_moe_impl_class(quant_config)(
-            num_experts=config.num_experts + get_server_args().ep_num_redundant_experts,
+            num_experts=config.num_experts + get_exec().moe.ep_num_redundant_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
@@ -206,7 +206,7 @@ class ExaoneMoESparseMoEBlock(nn.Module):
         if get_moe_a2a_backend().is_deepep():
             self.ep_size = get_parallel().moe_ep_size
             self.num_experts = (
-                config.num_experts + get_server_args().ep_num_redundant_experts
+                config.num_experts + get_exec().moe.ep_num_redundant_experts
             )
             self.top_k = config.num_experts_per_tok
 

python/sglang/srt/models/gemma4_causal.py

@@ -18,11 +18,7 @@ from typing import Iterable, List, Optional, Set, Tuple, Union
 
 import torch
 from torch import nn
-from transformers import (
-    Gemma4TextConfig,
-    PretrainedConfig,
-    PreTrainedModel,
-)
+from transformers import Gemma4TextConfig, PretrainedConfig, PreTrainedModel
 
 from sglang.kernels.ops.layernorm.gemma4_fused_ops import (
     gemma4_fused_routing,
@@ -31,9 +27,7 @@ from sglang.kernels.ops.layernorm.gemma4_fused_ops import (
     gemma_rmsnorm_residual_scalar,
     gemma_routing_post_topk,
 )
-from sglang.srt.distributed import (
-    get_pp_group,
-)
+from sglang.srt.distributed import get_pp_group
 from sglang.srt.layers.layernorm import Gemma4RMSNorm, RMSNorm
 from sglang.srt.layers.linear import (
     QKVParallelLinear,
@@ -55,10 +49,8 @@ from sglang.srt.model_loader.weight_utils import (
     maybe_remap_kv_scale_name,
 )
 from sglang.srt.models.gemma3_causal import Gemma3MLP, Gemma3TextScaledWordEmbedding
-from sglang.srt.models.utils import (
-    create_fused_set_kv_buffer_arg,
-)
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.models.utils import create_fused_set_kv_buffer_arg
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args
 from sglang.srt.utils import add_prefix, make_layers
 
 logger = logging.getLogger(__name__)
@@ -254,7 +246,7 @@ class Gemma4MoE(nn.Module):
         experts_type = get_moe_impl_class(quant_config)
 
         self.experts = experts_type(
-            num_experts=config.num_experts + get_server_args().ep_num_redundant_experts,
+            num_experts=config.num_experts + get_exec().moe.ep_num_redundant_experts,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
             layer_id=layer_id,

python/sglang/srt/models/gemma4_vision.py

@@ -29,7 +29,7 @@ from sglang.srt.layers.clippable_linear import (
 )
 from sglang.srt.layers.layernorm import Gemma4RMSNorm
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_mm, get_parallel
 from sglang.srt.utils import add_prefix, get_device_capability, is_cuda, is_hip
 
 # ---------------------------------------------------------------------------
@@ -181,9 +181,8 @@ class Gemma4VisionAttention(nn.Module):
     @staticmethod
     def _select_backend() -> str:
         """Mirror VisionAttention._determine_attention_backend for consistency."""
-        from sglang.srt.runtime_context import get_server_args
 
-        override = get_server_args().mm_attention_backend
+        override = get_mm().mm_attention_backend
         if override is not None:
             return override
         if is_cuda():

python/sglang/srt/models/glm4_moe.py

@@ -84,6 +84,7 @@ from sglang.srt.models.deepseek_nextn import DeepseekV3ForCausalLMNextN
 from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM
 from sglang.srt.models.utils import WeightsMapper, apply_qk_norm
 from sglang.srt.runtime_context import (
+    get_exec,
     get_forward,
     get_parallel,
     get_server_args,
@@ -406,7 +407,7 @@ class Glm4MoeSparseMoeBlock(nn.Module):
         self.n_shared_experts = config.n_shared_experts
         self.num_fused_shared_experts = (
             0
-            if get_server_args().disable_shared_experts_fusion
+            if get_exec().moe.disable_shared_experts_fusion
             else config.n_shared_experts
         )
 
@@ -526,7 +527,7 @@ class Glm4MoeSparseMoeBlock(nn.Module):
             # TODO: we will support tp < ep in the future
             self.ep_size = get_parallel().moe_ep_size
             self.num_experts = (
-                config.n_routed_experts + get_server_args().ep_num_redundant_experts
+                config.n_routed_experts + get_exec().moe.ep_num_redundant_experts
             )
             self.renormalize = config.norm_topk_prob
             self.topk_group = config.topk_group
@@ -1178,7 +1179,7 @@ class Glm4MoeForCausalLM(nn.Module):
         self.capture_aux_hidden_states = False
 
     def determine_num_fused_shared_experts(self):
-        if get_server_args().disable_shared_experts_fusion:
+        if get_exec().moe.disable_shared_experts_fusion:
             return
 
         disable_reason = None

python/sglang/srt/models/glm4_moe_lite.py

@@ -75,6 +75,7 @@ from sglang.srt.models.deepseek_common.deepseek_weight_loader import (
 from sglang.srt.models.deepseek_common.utils import _is_cuda, _use_aiter
 from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
 from sglang.srt.runtime_context import (
+    get_exec,
     get_forward,
     get_parallel,
     get_server_args,
@@ -189,7 +190,7 @@ class Glm4MoeLiteSparseMoeBlock(nn.Module):
         self.n_shared_experts = config.n_shared_experts
         self.num_fused_shared_experts = (
             0
-            if get_server_args().disable_shared_experts_fusion
+            if get_exec().moe.disable_shared_experts_fusion
             else config.n_shared_experts
         )
         self.config = config
@@ -216,7 +217,7 @@ class Glm4MoeLiteSparseMoeBlock(nn.Module):
         self.experts = get_moe_impl_class(quant_config)(
             num_experts=config.n_routed_experts
             + self.num_fused_shared_experts
-            + get_server_args().ep_num_redundant_experts,
+            + get_exec().moe.ep_num_redundant_experts,
             num_fused_shared_experts=self.num_fused_shared_experts,
             top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
             hidden_size=config.hidden_size,
@@ -284,7 +285,7 @@ class Glm4MoeLiteSparseMoeBlock(nn.Module):
             # TODO: we will support tp < ep in the future
             self.ep_size = get_parallel().moe_ep_size
             self.num_experts = (
-                config.n_routed_experts + get_server_args().ep_num_redundant_experts
+                config.n_routed_experts + get_exec().moe.ep_num_redundant_experts
             )
             self.renormalize = config.norm_topk_prob
             self.topk_group = config.topk_group
@@ -928,7 +929,7 @@ class Glm4MoeLiteForCausalLM(nn.Module, DeepseekV2WeightLoaderMixin):
         self, architecture: str = "Glm4MoeLiteForCausalLM"
     ):
         self.num_fused_shared_experts = 0
-        if get_server_args().disable_shared_experts_fusion:
+        if get_exec().moe.disable_shared_experts_fusion:
             return
 
         disable_reason = None

python/sglang/srt/models/glm4_moe_lite_nextn.py

@@ -35,7 +35,7 @@ from sglang.srt.models.glm4_moe_lite import (
     Glm4MoeLiteDecoderLayer,
     Glm4MoeLiteForCausalLM,
 )
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args, get_spec
 from sglang.srt.utils import BumpAllocator, add_prefix, is_npu
 
 logger = logging.getLogger(__name__)
@@ -139,7 +139,7 @@ class Glm4MoeLiteForCausalLMNextN(Glm4MoeLiteForCausalLM):
         nn.Module.__init__(self)
         self.config = config
         self.tp_size = get_parallel().tp_size
-        if is_npu() and get_server_args().speculative_draft_model_quantization is None:
+        if is_npu() and get_spec().speculative_draft_model_quantization is None:
             quant_config = None
         self.quant_config = quant_config
 
@@ -156,7 +156,7 @@ class Glm4MoeLiteForCausalLMNextN(Glm4MoeLiteForCausalLM):
         self.logits_processor = LogitsProcessor(config)
 
         self.num_fused_shared_experts = (
-            0 if get_server_args().disable_shared_experts_fusion else 1
+            0 if get_exec().moe.disable_shared_experts_fusion else 1
         )
 
     @torch.no_grad()

python/sglang/srt/models/glm4_moe_nextn.py

@@ -32,7 +32,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.models.glm4_moe import Glm4MoeDecoderLayer, Glm4MoeForCausalLM
-from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.runtime_context import get_exec, get_parallel, get_server_args, get_spec
 from sglang.srt.utils import add_prefix, is_npu
 
 logger = logging.getLogger(__name__)
@@ -125,7 +125,7 @@ class Glm4MoeForCausalLMNextN(Glm4MoeForCausalLM):
         nn.Module.__init__(self)
         self.config = config
         self.tp_size = get_parallel().tp_size
-        if is_npu() and get_server_args().speculative_draft_model_quantization is None:
+        if is_npu() and get_spec().speculative_draft_model_quantization is None:
             quant_config = None
         self.quant_config = quant_config
 
@@ -142,7 +142,7 @@ class Glm4MoeForCausalLMNextN(Glm4MoeForCausalLM):
         self.logits_processor = LogitsProcessor(config)
 
         self.num_fused_shared_experts = (
-            0 if get_server_args().disable_shared_experts_fusion else 1
+            0 if get_exec().moe.disable_shared_experts_fusion else 1
         )
 
     @torch.no_grad()