Deprecate `global_server_args_dict` (#11331)

python/sglang/srt/models/qwen3_next.py

@@ -39,7 +39,6 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import (
@@ -47,6 +46,7 @@ from sglang.srt.model_loader.weight_utils import (
     sharded_weight_loader,
 )
 from sglang.srt.models.qwen2_moe import Qwen2MoeMLP, Qwen2MoeSparseMoeBlock
+from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import (
     LazyValue,
     add_prefix,
@@ -905,7 +905,7 @@ class Qwen3NextForCausalLM(nn.Module):
             quant_config=quant_config,
             org_num_embeddings=config.vocab_size,
             prefix=add_prefix("lm_head", prefix),
-            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+            use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
         )
         self.lm_head = self.lm_head.float()
         self.logits_processor = LogitsProcessor(config)

python/sglang/srt/models/qwen3_next_mtp.py

@@ -21,14 +21,13 @@ from torch import nn
 from transformers import PretrainedConfig
 
 from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
-from sglang.srt.layers.layernorm import GemmaRMSNorm, RMSNorm
+from sglang.srt.layers.layernorm import GemmaRMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
-from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.models.qwen3_moe import Qwen3MoeModel
 from sglang.srt.models.qwen3_next import Qwen3NextForCausalLM, Qwen3NextModel
+from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import add_prefix
 
 logger = logging.getLogger(__name__)
@@ -69,7 +68,7 @@ class Qwen3NextForCausalLMMTP(Qwen3NextForCausalLM):
             config.hidden_size,
             quant_config=quant_config,
             prefix=add_prefix("model.shared_head.head", prefix),
-            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+            use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
         )
         self.logits_processor = LogitsProcessor(config)
 

python/sglang/srt/models/qwen3_vl_moe.py

@@ -38,20 +38,12 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
 from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
-from sglang.srt.layers.utils import get_layer_id
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
-from sglang.srt.managers.mm_utils import (
-    MultiModalityDataPaddingPatternMultimodalTokens,
-    general_mm_embed_routine,
-)
-from sglang.srt.managers.schedule_batch import (
-    MultimodalDataItem,
-    MultimodalInputs,
-    global_server_args_dict,
-)
+from sglang.srt.managers.mm_utils import general_mm_embed_routine
+from sglang.srt.managers.schedule_batch import MultimodalDataItem
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.models.qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from sglang.srt.models.qwen3_moe import Qwen3MoeModel
 from sglang.srt.models.qwen3_vl import (
     Qwen3_VisionTransformer,
     Qwen3VLForConditionalGeneration,

python/sglang/srt/models/step3_vl.py

@@ -57,7 +57,6 @@ from sglang.srt.managers.schedule_batch import (
     Modality,
     MultimodalDataItem,
     MultimodalInputs,
-    global_server_args_dict,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
@@ -300,7 +299,7 @@ class Step3TextDecoderLayer(nn.Module):
         # self.n_shared_experts = 1
         # self.num_fused_shared_experts = (
         #     0
-        #     if global_server_args_dict["disable_shared_experts_fusion"]
+        #     if global_server_args.disable_shared_experts_fusion
         #     else self.n_shared_experts
         # )
         self.num_fused_shared_experts = 0
@@ -774,7 +773,7 @@ class Step3VLForConditionalGeneration(nn.Module):
         # self.n_shared_experts = 1
         # self.num_fused_shared_experts = (
         #     0
-        #     if global_server_args_dict["disable_shared_experts_fusion"]
+        #     if global_server_args.disable_shared_experts_fusion
         #     else self.n_shared_experts
         # )
         self.num_fused_shared_experts = 0

python/sglang/srt/sampling/sampling_batch_info.py

@@ -2,7 +2,6 @@ from __future__ import annotations
 
 import dataclasses
 import logging
-import threading
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
 
 import torch
@@ -10,6 +9,7 @@ import torch
 import sglang.srt.sampling.penaltylib as penaltylib
 from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
 from sglang.srt.sampling.sampling_params import TOP_K_ALL
+from sglang.srt.server_args import get_global_server_args
 
 if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import ScheduleBatch
@@ -66,16 +66,10 @@ class SamplingBatchInfo:
     # Handle logit bias
     logit_bias: Optional[torch.Tensor] = None
 
-    @classmethod
-    def _get_global_server_args_dict(cls):
-        from sglang.srt.managers.schedule_batch import global_server_args_dict
-
-        return global_server_args_dict
-
     @classmethod
     def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
-        global_server_args_dict = cls._get_global_server_args_dict()
-        enable_deterministic = global_server_args_dict["enable_deterministic_inference"]
+        global_server_args = get_global_server_args()
+        enable_deterministic = global_server_args.enable_deterministic_inference
 
         reqs = batch.reqs
         device = batch.device
@@ -112,10 +106,9 @@ class SamplingBatchInfo:
                         logit_bias[i, int(key)] = value
 
         # Check if any request has custom logit processor
-        has_custom_logit_processor = global_server_args_dict[
-            "enable_custom_logit_processor"
-        ] and any(  # check the flag first.
-            r.custom_logit_processor for r in reqs
+        has_custom_logit_processor = (
+            global_server_args.enable_custom_logit_processor
+            and any(r.custom_logit_processor for r in reqs)  # check the flag first.
         )  # then check the requests.
 
         if has_custom_logit_processor:

python/sglang/srt/server_args.py

@@ -53,6 +53,7 @@ from sglang.utils import is_in_ci
 
 logger = logging.getLogger(__name__)
 
+
 # Define constants
 LOAD_FORMAT_CHOICES = [
     "auto",
@@ -3329,6 +3330,22 @@ class ServerArgs:
         )
 
 
+# NOTE: This is a global variable to hold the server args for scheduler.
+_global_server_args: Optional[ServerArgs] = None
+
+
+def set_global_server_args_for_scheduler(server_args: ServerArgs):
+    global _global_server_args
+    _global_server_args = server_args
+
+
+def get_global_server_args() -> ServerArgs:
+    if _global_server_args is None:
+        raise ValueError("Global server args is not set yet!")
+
+    return _global_server_args
+
+
 def prepare_server_args(argv: List[str]) -> ServerArgs:
     """
     Prepare the server arguments from the command line arguments.
@@ -3363,8 +3380,8 @@ def prepare_server_args(argv: List[str]) -> ServerArgs:
     parser = argparse.ArgumentParser()
     ServerArgs.add_cli_args(parser)
     raw_args = parser.parse_args(argv)
-    server_args = ServerArgs.from_cli_args(raw_args)
-    return server_args
+
+    return ServerArgs.from_cli_args(raw_args)
 
 
 ZMQ_TCP_PORT_DELTA = 233

python/sglang/srt/single_batch_overlap.py

@@ -6,7 +6,6 @@ import torch
 from sglang.srt.layers.moe import get_moe_runner_backend
 from sglang.srt.layers.moe.utils import is_sbo_enabled
 from sglang.srt.layers.quantization import deep_gemm_wrapper
-from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import get_int_env_var
 

python/sglang/srt/speculative/eagle_info.py

@@ -11,7 +11,7 @@ from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_trito
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import apply_custom_logit_processor
 from sglang.srt.managers.overlap_utils import FutureIndices
-from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
+from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.common import (
     alloc_paged_token_slots_extend,
@@ -19,6 +19,7 @@ from sglang.srt.mem_cache.common import (
     get_last_loc,
 )
 from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode
+from sglang.srt.server_args import get_global_server_args
 from sglang.srt.speculative.eagle_info_v2 import (
     EagleDraftInputV2Mixin,
     EagleVerifyInputV2Mixin,
@@ -332,12 +333,8 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
                 uniform_samples_for_final_sampling=coins_for_final_sampling,
                 target_probs=target_probs,
                 draft_probs=draft_probs,
-                threshold_single=global_server_args_dict[
-                    "speculative_accept_threshold_single"
-                ],
-                threshold_acc=global_server_args_dict[
-                    "speculative_accept_threshold_acc"
-                ],
+                threshold_single=get_global_server_args().speculative_accept_threshold_single,
+                threshold_acc=get_global_server_args().speculative_accept_threshold_acc,
                 deterministic=True,
             )
 

python/sglang/srt/speculative/eagle_info_v2.py

@@ -11,7 +11,6 @@ import triton.language as tl
 
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch
-from sglang.srt.managers.scheduler import global_server_args_dict
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
 from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
@@ -19,6 +18,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
 )
 from sglang.srt.model_executor.model_runner import ModelRunner
+from sglang.srt.server_args import get_global_server_args
 from sglang.srt.speculative.build_eagle_tree import TreeMaskMode
 from sglang.srt.speculative.spec_utils import (
     SIMULATE_ACC_LEN,
@@ -265,12 +265,8 @@ class EagleVerifyInputV2Mixin:
                 uniform_samples_for_final_sampling=coins_for_final_sampling,
                 target_probs=target_probs,
                 draft_probs=draft_probs,
-                threshold_single=global_server_args_dict[
-                    "speculative_accept_threshold_single"
-                ],
-                threshold_acc=global_server_args_dict[
-                    "speculative_accept_threshold_acc"
-                ],
+                threshold_single=get_global_server_args().speculative_accept_threshold_single,
+                threshold_acc=get_global_server_args().speculative_accept_threshold_acc,
                 deterministic=True,
             )
 

python/sglang/srt/speculative/eagle_worker.py

@@ -14,7 +14,7 @@ from sglang.srt.distributed import (
 )
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import get_token_ids_logprobs, get_top_logprobs
-from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
+from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.managers.scheduler import GenerationBatchResult
 from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.mem_cache.common import (
@@ -27,7 +27,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardBatch,
     ForwardMode,
 )
-from sglang.srt.server_args import ServerArgs
+from sglang.srt.server_args import ServerArgs, get_global_server_args
 from sglang.srt.speculative.build_eagle_tree import build_tree_kernel_efficient
 from sglang.srt.speculative.eagle_draft_cuda_graph_runner import (
     EAGLEDraftCudaGraphRunner,
@@ -261,7 +261,7 @@ class EAGLEWorker(TpModelWorker):
         )
 
     def _create_flashinfer_decode_backend(self):
-        if not global_server_args_dict["use_mla_backend"]:
+        if not get_global_server_args().use_mla_backend:
             from sglang.srt.layers.attention.flashinfer_backend import (
                 FlashInferMultiStepDraftBackend,
             )
@@ -325,7 +325,7 @@ class EAGLEWorker(TpModelWorker):
         )
 
     def _create_trtllm_mla_decode_backend(self):
-        if not global_server_args_dict["use_mla_backend"]:
+        if not get_global_server_args().use_mla_backend:
             raise ValueError(
                 "trtllm_mla backend requires MLA model (use_mla_backend=True)."
             )
@@ -340,7 +340,7 @@ class EAGLEWorker(TpModelWorker):
         )
 
     def _create_flashinfer_prefill_backend(self):
-        if not global_server_args_dict["use_mla_backend"]:
+        if not get_global_server_args().use_mla_backend:
             from sglang.srt.layers.attention.flashinfer_backend import (
                 FlashInferAttnBackend,
             )
@@ -376,7 +376,7 @@ class EAGLEWorker(TpModelWorker):
         return TRTLLMHAAttnBackend(self.draft_model_runner, skip_prefill=False)
 
     def _create_trtllm_mla_prefill_backend(self):
-        if not global_server_args_dict["use_mla_backend"]:
+        if not get_global_server_args().use_mla_backend:
             raise ValueError(
                 "trtllm_mla backend requires MLA model (use_mla_backend=True)."
             )

python/sglang/srt/speculative/ngram_info.py

@@ -7,6 +7,8 @@ from typing import Optional, Tuple
 import torch
 import triton
 
+from sglang.srt.server_args import get_global_server_args
+
 logger = logging.getLogger(__name__)
 
 from dataclasses import dataclass
@@ -16,7 +18,7 @@ import torch.nn.functional as F
 from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import apply_custom_logit_processor
-from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
+from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.mem_cache.common import (
     alloc_paged_token_slots_extend,
     alloc_token_slots,
@@ -350,10 +352,8 @@ class NgramVerifyInput(SpecInput):
             uniform_samples_for_final_sampling=coins_for_final_sampling,
             target_probs=target_probs,
             draft_probs=draft_probs,
-            threshold_single=global_server_args_dict[
-                "speculative_accept_threshold_single"
-            ],
-            threshold_acc=global_server_args_dict["speculative_accept_threshold_acc"],
+            threshold_single=get_global_server_args().speculative_accept_threshold_single,
+            threshold_acc=get_global_server_args().speculative_accept_threshold_acc,
             deterministic=True,
         )
 

python/sglang/srt/two_batch_overlap.py

@@ -22,7 +22,7 @@ from sglang.srt.layers.moe import (
 )
 from sglang.srt.layers.moe.token_dispatcher import DeepEPDispatcher
 from sglang.srt.layers.quantization import deep_gemm_wrapper
-from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
+from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.model_executor.forward_batch_info import (
     ForwardBatch,
     ForwardMode,
@@ -30,6 +30,7 @@ from sglang.srt.model_executor.forward_batch_info import (
 )
 from sglang.srt.operations import execute_operations, execute_overlapped_operations
 from sglang.srt.operations_strategy import OperationsStrategy
+from sglang.srt.server_args import get_global_server_args
 from sglang.srt.speculative.spec_info import SpecInput
 from sglang.srt.utils import BumpAllocator, empty_context, get_bool_env_var, is_hip
 
@@ -153,7 +154,7 @@ 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=global_server_args_dict["device"], non_blocking=True)
+    ).to(device=get_global_server_args().device, non_blocking=True)
     setattr(batch, device_field, new_device_value)
 
     if sum_field is not None:
@@ -582,7 +583,7 @@ class TboForwardBatchPreparer:
             sum_field=None,
         )
         _, child_b.extend_start_loc = compute_position(
-            global_server_args_dict["attention_backend"],
+            get_global_server_args().attention_backend,
             child_b.extend_prefix_lens,
             child_b.extend_seq_lens,
             child_b.extend_num_tokens,
@@ -687,7 +688,7 @@ class TboForwardBatchPreparer:
 
         # TODO improve, e.g. unify w/ `init_raw`
         if (
-            global_server_args_dict["moe_dense_tp_size"] == 1
+            get_global_server_args().moe_dense_tp_size == 1
             and batch.global_dp_buffer_len is not None
         ):
             sum_len = end_token_index - start_token_index
@@ -755,7 +756,7 @@ 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=global_server_args_dict["device"], non_blocking=True
+            device=get_global_server_args().device, non_blocking=True
         )
 
     @classmethod

test/srt/rl/test_fp32_lm_head.py

@@ -7,7 +7,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.server_args import (
+    ServerArgs,
+    get_global_server_args,
+    set_global_server_args_for_scheduler,
+)
 
 
 class LMHeadStub(nn.Module):
@@ -32,8 +36,10 @@ class TestLMHeadFP32(unittest.TestCase):
             raise unittest.SkipTest("needs CUDA GPU")
 
     def _make_logprocessor(self, vocab_size, enable_fp32):
-        global_server_args_dict["enable_dp_lm_head"] = False
-        global_server_args_dict["enable_fp32_lm_head"] = enable_fp32
+        ServerArgs.__post_init__ = lambda self: None  # disable validation
+        set_global_server_args_for_scheduler(ServerArgs(model_path="dummy"))
+        get_global_server_args().enable_dp_lm_head = False
+        get_global_server_args().enable_fp32_lm_head = enable_fp32
         cfg = SimpleNamespace(vocab_size=vocab_size, final_logit_softcapping=None)
         return LogitsProcessor(cfg, skip_all_gather=True, logit_scale=None)
 

test/srt/test_gptqmodel_dynamic.py

@@ -4,6 +4,7 @@ import unittest
 import requests
 import torch
 
+from sglang.srt.server_args import set_global_server_args_for_scheduler
 from sglang.srt.utils import kill_process_tree
 from sglang.test.test_utils import (
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
@@ -16,17 +17,15 @@ from sglang.test.test_utils import (
 def check_quant_method(model_path: str, use_marlin_kernel: bool):
     from sglang.srt.configs.device_config import DeviceConfig
     from sglang.srt.configs.load_config import LoadConfig
-    from sglang.srt.configs.model_config import AttentionArch, ModelConfig
+    from sglang.srt.configs.model_config import ModelConfig
     from sglang.srt.distributed import (
-        get_tp_group,
         init_distributed_environment,
         initialize_model_parallel,
-        set_custom_all_reduce,
     )
     from sglang.srt.distributed.parallel_state import monkey_patch_vllm_parallel_state
     from sglang.srt.layers.quantization.utils import get_dynamic_override
     from sglang.srt.model_loader import get_model
-    from sglang.srt.server_args import PortArgs, ServerArgs
+    from sglang.srt.server_args import ServerArgs
 
     try:
         init_distributed_environment(
@@ -43,6 +42,7 @@ def check_quant_method(model_path: str, use_marlin_kernel: bool):
         pass
 
     server_args = ServerArgs(model_path=model_path, dtype=torch.float16)
+    set_global_server_args_for_scheduler(server_args)
     model_config = ModelConfig.from_server_args(server_args)
 
     load_config = LoadConfig()