config: the post-publish consumers of the supplied-instance surface read the bags

config: the speculative workers take page_size from the bags

Seven worker constructors stored `self.page_size = server_args.page_size` off
the handed record. They all run after publish and all keep a copy of a
process-level value, which is the first row of the plan doc's supplied-instance
disposition table -- so they read `get_schedule().page_size`, and a post-publish
override now reaches them like it reaches every other consumer.

The supplied-instance census named the seven pairs; the exposure ratchet in the
next member pins what remains after this batch of conversions.


config: the post-publish chunked_prefill_size consumers read the bags

Four of the ten supplied-instance `chunked_prefill_size` reads are plain
post-publish consumers -- the EPLB recorder's buffer sizing, the deep-gemm
compile warmup (five reads), the KV-cache builder's effective size, and the
ngram embedding manager's assert. All are reached from runner init, so they read
`get_schedule()`.

Two are deliberately left: `create_kt_config_from_server_args` builds a config
*from a supplied record* by name and contract, and `CanaryLaunchCapacities.from_args`
is the same shape. Converting those would change what the function is, not where
it reads -- the plan doc's disposition table says so per field.


config: the remaining post-publish graph/limit consumers read the bags

Three more of the census's supplied-instance debts are plain post-publish reads: the dspark worker's
cuda-graph decode sizes, the dspark planner's SPS table bound
(`max_running_requests`), and the LoRA manager's cuda-graph moe buffers. The
dspark worker is the clearest of them -- it already read
`get_exec().graph.cuda_graph_config.decode.bs` thirty lines below the instance
read, so the file disagreed with itself about where the same value comes from.

Left where the function's contract is "build a config from the record you are
handed" rather than "read this process's config":
`create_kt_config_from_server_args`, `DllmConfig.from_server_args`,
`CanaryLaunchCapacities.from_args`, `build_compilation_config`. Changing those
would change what the function is.


config: the runner, scheduler and offload manager take page_size from the bags

The same `self.page_size = server_args.page_size` shape as the speculative
workers, in the three remaining process-owned constructors: `ModelRunner`,
`Scheduler`, and the decode-side KV offload manager. The scheduler process
publishes before any of them run. The one path that did not is `ModelRunner`
constructed standalone -- `python -m sglang.benchmark.one_batch` and the manual
runner tests build it with no prior publish, and the constructor's own publish
sat below this read -- so that publish moves above the constructor's first bag
read instead of leaving a window where the runner half-exists unpublished.

Left where the read belongs to something else: `utils/common`'s predicates are
called only from the resolution pipeline with a `resolved_view`,
`allocation_sizing` takes the config its callers supply by contract, and
`CudaVmmFeatureTransport` is tokenizer-owned -- one per tokenizer worker, which
is the per-instance boundary.

The conversion left the offload manager parking a record it no longer
reads; the parked copy goes with the read (the constructor parameter stays
-- its hicache sizing still reads it directly).

python/sglang/srt/disaggregation/decode_kvcache_offload_manager.py

@@ -21,6 +21,7 @@ from sglang.srt.mem_cache.memory_pool import (
 from sglang.srt.mem_cache.pool_host.common import get_allocator_type
 from sglang.srt.mem_cache.pool_host.mha import get_mha_host_pool_cls
 from sglang.srt.mem_cache.pool_host.mla import MLATokenToKVPoolHost
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.common import ceil_align
 
@@ -43,8 +44,7 @@ class DecodeKVCacheOffloadManager:
     ) -> None:
         self.req_to_token_pool = req_to_token_pool
         self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
-        self.page_size = server_args.page_size
-        self.server_args = server_args
+        self.page_size = get_schedule().page_size
         self.request_counter = 0
         self.tree_cache = tree_cache
         env_stride = envs.SGLANG_HICACHE_DECODE_OFFLOAD_STRIDE.get()

python/sglang/srt/eplb/expert_distribution.py

@@ -35,6 +35,7 @@ from sglang.srt.observability.metrics_collector import (
     ExpertDispatchCollector,
     resolve_collector_class,
 )
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import Withable, get_device, get_int_env_var
 
@@ -390,7 +391,7 @@ class _DetailSinglePassGatherer(_SinglePassGatherer):
             (
                 expert_location_metadata.num_layers,
                 # TODO determine the max number
-                server_args.chunked_prefill_size * 8,
+                get_schedule().chunked_prefill_size * 8,
                 self._TOP_K_NUM,
             ),
             dtype=torch.int32,

python/sglang/srt/layers/deep_gemm_wrapper/compile_utils.py

@@ -16,7 +16,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
 from sglang.srt.environ import envs
 from sglang.srt.layers.deep_gemm_wrapper.configurer import ENABLE_JIT_DEEPGEMM
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_parallel, get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import ceil_align, ceil_div, get_available_gpu_memory, is_musa
 
@@ -67,9 +67,10 @@ def update_deep_gemm_config(gpu_id: int, server_args: ServerArgs):
         #   8192, 9008, ... 16384 (step 16)
         # Totally 1024 + 1024 / 2 + 2048 / 4 + 4096 / 8 + 8192 / 16 = 3072 kernels
         next_m, sample_step = 1024, 2
+        chunked_prefill_size = get_schedule().chunked_prefill_size
         max_prefill_bs = (
-            min(server_args.chunked_prefill_size, 32 * 1024)
-            if server_args.chunked_prefill_size >= 1
+            min(chunked_prefill_size, 32 * 1024)
+            if chunked_prefill_size >= 1
             else 16 * 1024
         )
         while next_m < max_prefill_bs:
@@ -81,10 +82,11 @@ def update_deep_gemm_config(gpu_id: int, server_args: ServerArgs):
     else:
         # When fast warmup isn't enabled, generate m_max and compile all the covered Ms.
         m_max = 1024 * 16
-        if server_args.chunked_prefill_size < 1:
+        chunked_prefill_size = get_schedule().chunked_prefill_size
+        if chunked_prefill_size < 1:
             m_max = 1024 * 64
-        elif server_args.chunked_prefill_size > 8192:
-            m_max = server_args.chunked_prefill_size * 2
+        elif chunked_prefill_size > 8192:
+            m_max = chunked_prefill_size * 2
         m_max = min(1024 * 128, m_max)
         _BUILTIN_M_LIST += list(range(1, m_max + 1))
 

python/sglang/srt/lora/lora_manager.py

@@ -46,7 +46,7 @@ from sglang.srt.lora.utils import (
 )
 from sglang.srt.managers.io_struct import LoRAUpdateOutput
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_exec, get_parallel
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import get_available_gpu_memory, replace_submodule
 from sglang.srt.utils.hf_transformers_utils import AutoConfig
@@ -1030,7 +1030,7 @@ def init_lora_cuda_graph_moe_buffers(
     """
     from sglang.srt.lora.layers import FusedMoEWithLoRA
 
-    max_bs = server_args.cuda_graph_config.decode.max_bs
+    max_bs = get_exec().graph.cuda_graph_config.decode.max_bs
     max_loras = server_args.max_loras_per_batch
     for module in model.modules():
         if isinstance(module, FusedMoEWithLoRA):

python/sglang/srt/managers/scheduler.py

@@ -436,7 +436,7 @@ class Scheduler(
         self.spec_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
         self.enable_session_radix_cache = server_args.enable_session_radix_cache
         self.enable_hicache_storage = server_args.hicache_storage_backend is not None

python/sglang/srt/mem_cache/kv_cache_builder.py

@@ -36,7 +36,7 @@ from sglang.srt.managers.mm_schedule import init_mm_embedding_cache
 from sglang.srt.mem_cache.cache_init_params import CacheInitParams
 from sglang.srt.mem_cache.registry import TreeCacheBuildContext, create_tree_cache
 from sglang.srt.model_loader.utils import get_resolved_model_impl
-from sglang.srt.runtime_context import get_parallel
+from sglang.srt.runtime_context import get_parallel, get_schedule
 
 if TYPE_CHECKING:
 
@@ -205,7 +205,7 @@ def build_kv_cache(
                 "with Mamba/SSM models"
             )
 
-    effective_chunked_prefill_size = server_args.chunked_prefill_size
+    effective_chunked_prefill_size = get_schedule().chunked_prefill_size
     if model_config.is_multimodal and uses_transformers_backend:
         effective_chunked_prefill_size = None
 

python/sglang/srt/model_executor/model_runner.py

@@ -313,6 +313,13 @@ class ModelRunner:
         self.dist_port = nccl_port
         self.server_args = server_args
         self.is_draft_worker = is_draft_worker
+        # Set the global server_args in the scheduler process (target worker
+        # only, so a draft init cannot clobber target-derived global state).
+        # Before the constructor's bag reads (page_size below): a standalone
+        # construction (benchmark/one_batch, the manual runner tests) has no
+        # earlier publish.
+        if not is_draft_worker:
+            set_global_server_args_for_scheduler(server_args)
         self.draft_attention_backend = resolve_draft_attention_backend(
             draft_attention_backend=draft_attention_backend,
             server_args=server_args,
@@ -332,7 +339,7 @@ class ModelRunner:
             server_args.speculative_algorithm
         )
         self.capture_tail_hooks = []
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.req_to_token_pool = req_to_token_pool
         self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
         self.mtp_draft_device_pools = ()
@@ -360,11 +367,6 @@ class ModelRunner:
         if server_args.show_time_cost:
             enable_show_time_cost()
 
-        # Set the global server_args in the scheduler process (target worker
-        # only, so a draft init cannot clobber target-derived global state).
-        if not self.is_draft_worker:
-            set_global_server_args_for_scheduler(server_args)
-
         misc_utils.maybe_disable_chunked_prefix_cache(
             use_mla_backend=self.use_mla_backend,
             is_draft_worker=self.is_draft_worker,

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

@@ -11,6 +11,7 @@ from sglang.kernels.ops.speculative.ngram_embedding import update_token_table
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.managers.schedule_batch import ForwardMode
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 
 if TYPE_CHECKING:
@@ -50,7 +51,7 @@ class NgramEmbeddingManager:
                 dtype=torch.int32,
                 device=device,
             )
-            chunked_prefill_size = server_args.chunked_prefill_size
+            chunked_prefill_size = get_schedule().chunked_prefill_size
             assert (
                 chunked_prefill_size is not None and chunked_prefill_size > 0
             ), "Ngram embedding requires chunked prefill to be enabled (chunked_prefill_size > 0)"

python/sglang/srt/speculative/dflash_worker_v2.py

@@ -28,7 +28,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
     compute_position,
 )
-from sglang.srt.runtime_context import get_exec
+from sglang.srt.runtime_context import get_exec, get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker
 from sglang.srt.speculative.dflash_info import DFlashVerifyInput
@@ -189,7 +189,7 @@ class DFlashWorkerV2(BaseSpecWorker):
         self._target_worker = target_worker
         self.model_runner = target_worker.model_runner
         self._need_mamba_verify_commit = False
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         # Normalized in arg_groups.speculative_hook.handle_speculative_decoding.
         self.draft_window_size: Optional[int] = (
             server_args.speculative_draft_window_size

python/sglang/srt/speculative/dspark_components/dspark_planner.py

@@ -1139,6 +1139,6 @@ def build_sps_cost_table(
         return load_sps_table_from_path(sps_table_path)
     max_batch_tokens = max(
         1,
-        int(server_args.max_running_requests or 1) * verify_num_draft_tokens,
+        int(get_schedule().max_running_requests or 1) * verify_num_draft_tokens,
     )
     return build_uninitialized_sps_table(max_batch_tokens=max_batch_tokens)

python/sglang/srt/speculative/dspark_components/dspark_worker_v2.py

@@ -18,7 +18,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
     compute_position,
 )
-from sglang.srt.runtime_context import get_exec, get_parallel, get_spec
+from sglang.srt.runtime_context import get_exec, get_parallel, get_schedule, get_spec
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker
 from sglang.srt.speculative.dflash_info_v2 import DFlashDraftInputV2
@@ -88,7 +88,7 @@ class DSparkWorkerV2(BaseSpecWorker):
         self.nccl_port = nccl_port
         self._target_worker = target_worker
         self.model_runner = target_worker.model_runner
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.device = target_worker.device
 
         self._draft_is_moe = draft_is_deepseek_v4(server_args=server_args)
@@ -229,7 +229,7 @@ class DSparkWorkerV2(BaseSpecWorker):
             and is_cuda()
         ):
             self._verify_epilogue = DsparkVerifyEpilogue(
-                max_bs=max(server_args.cuda_graph_config.decode.bs),
+                max_bs=max(get_exec().graph.cuda_graph_config.decode.bs),
                 verify_num_draft_tokens=self.verify_num_draft_tokens,
                 device=self.device,
                 commit_ctx=CommitInjectCtx(

python/sglang/srt/speculative/eagle_worker_v2.py

@@ -50,6 +50,7 @@ from sglang.srt.runtime_context import (
     get_exec,
     get_model,
     get_parallel,
+    get_schedule,
     get_spec,
 )
 from sglang.srt.server_args import ServerArgs
@@ -1024,7 +1025,7 @@ class EAGLEWorkerV2(BaseSpecWorker):
         self.gpu_id = gpu_id
         self.device = server_args.device
         self._target_worker = target_worker
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )

python/sglang/srt/speculative/frozen_kv_mtp_worker_v2.py

@@ -44,7 +44,7 @@ from sglang.srt.model_executor.forward_batch_info import (
 )
 from sglang.srt.model_executor.forward_context import ForwardContext, forward_context
 from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
-from sglang.srt.runtime_context import attention_backends, get_spec
+from sglang.srt.runtime_context import attention_backends, get_schedule, get_spec
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker, EagleDraftWorkerBase
 from sglang.srt.speculative.eagle_utils import (
@@ -109,7 +109,7 @@ class FrozenKVMTPDraftWorker(EagleDraftWorkerBase, TpModelWorker):
         self.gpu_id = gpu_id
         self.device = server_args.device
         self.target_worker = target_worker
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )
@@ -697,7 +697,7 @@ class FrozenKVMTPWorkerV2(EAGLEWorkerV2):
         self.gpu_id = gpu_id
         self.device = server_args.device
         self._target_worker = target_worker
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )

python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py

@@ -43,6 +43,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
     ForwardBatch,
 )
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker, EagleDraftWorkerBase
 from sglang.srt.speculative.draft_utils import DraftBackendFactory
@@ -933,7 +934,7 @@ class MultiLayerEagleWorkerV2(BaseSpecWorker):
         self.gpu_id = gpu_id
         self.device = server_args.device
         self._target_worker = target_worker
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )

python/sglang/srt/speculative/ngram_worker.py

@@ -15,6 +15,7 @@ from sglang.srt.managers.scheduler import GenerationBatchResult
 from sglang.srt.managers.tp_worker import TpModelWorker
 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_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker, EagleDraftWorkerBase
 from sglang.srt.speculative.cpp_ngram.ngram_corpus import NgramCorpus
@@ -91,7 +92,7 @@ class NGRAMWorker(BaseSpecWorker):
         self._target_worker = target_worker
         self.model_runner = target_worker.model_runner
         self.tp_rank = ps.tp_rank
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.draft_token_num: int = server_args.speculative_num_draft_tokens
         self.max_trie_depth: int = server_args.speculative_ngram_max_trie_depth
         self.speculative_num_draft_tokens = server_args.speculative_num_draft_tokens

python/sglang/srt/speculative/standalone_worker_v2.py

@@ -10,6 +10,7 @@ from sglang.srt.layers.moe.utils import (
     speculative_moe_backend_context,
 )
 from sglang.srt.managers.tp_worker import TpModelWorker
+from sglang.srt.runtime_context import get_schedule
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.adaptive_runtime_state import (
     AdaptiveController,
@@ -163,7 +164,7 @@ class StandaloneWorkerV2(EAGLEWorkerV2):
         self.gpu_id = gpu_id
         self.device = server_args.device
         self._target_worker = target_worker
-        self.page_size = server_args.page_size
+        self.page_size = get_schedule().page_size
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )