Revert "Deprecate global_server_args_dict" (#11520)

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

@@ -5,8 +5,8 @@ from typing import TYPE_CHECKING
 import torch
 
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.server_args import get_global_server_args
 
 if TYPE_CHECKING:
     from sglang.srt.layers.radix_attention import RadixAttention
@@ -42,7 +42,7 @@ class DoubleSparseAttnBackend(AttentionBackend):
         # TODO: Change the hard-coded block_seq_num
         self.BLOCK_SEQ = 128
 
-        if get_global_server_args().triton_attention_reduce_in_fp32:
+        if global_server_args_dict.get("triton_attention_reduce_in_fp32", False):
             self.reduce_dtype = torch.float32
         else:
             self.reduce_dtype = torch.float16

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

@@ -11,8 +11,8 @@ import triton.language as tl
 from sglang.srt.configs.model_config import AttentionArch
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.radix_attention import AttentionType
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.speculative.spec_info import SpecInput
 
 if TYPE_CHECKING:
@@ -830,7 +830,7 @@ class FlashAttentionBackend(AttentionBackend):
             ):
                 # Do multi-head attention with chunked prefix cache
                 if forward_batch.attn_attend_prefix_cache:
-                    assert not get_global_server_args().disable_chunked_prefix_cache
+                    assert not global_server_args_dict["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

@@ -28,8 +28,8 @@ from sglang.srt.layers.attention.flashinfer_backend import (
     create_flashinfer_kv_indices_triton,
 )
 from sglang.srt.layers.dp_attention import get_attention_tp_size
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.speculative.spec_info import SpecInput
 from sglang.srt.utils import (
     is_flashinfer_available,
@@ -193,9 +193,9 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         self.skip_prefill = skip_prefill
         self.enable_chunk_kv = (
             not skip_prefill
-            and get_global_server_args().disaggregation_mode != "decode"
-            and not get_global_server_args().disable_chunked_prefix_cache
-            and not get_global_server_args().flashinfer_mla_disable_ragged
+            and global_server_args_dict["disaggregation_mode"] != "decode"
+            and not global_server_args_dict["disable_chunked_prefix_cache"]
+            and not global_server_args_dict["flashinfer_mla_disable_ragged"]
         )
         self.page_size = model_runner.page_size
 
@@ -306,7 +306,7 @@ 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_global_server_args().flashinfer_mla_disable_ragged
+                not global_server_args_dict["flashinfer_mla_disable_ragged"]
                 and extend_no_prefix
             )
 

python/sglang/srt/layers/attention/nsa/nsa_indexer.py

@@ -23,9 +23,9 @@ from sglang.srt.layers.linear import ReplicatedLinear
 from sglang.srt.layers.quantization import deep_gemm_wrapper
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.rotary_embedding import get_rope_wrapper
+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.server_args import get_global_server_args
 
 if TYPE_CHECKING:
     from sglang.srt.mem_cache.memory_pool import NSATokenToKVPool
@@ -162,7 +162,7 @@ class Indexer(CustomOp):
             base=rope_theta,  # type: ignore
             rope_scaling=rope_scaling,
             is_neox_style=False,
-            device=get_global_server_args().device,
+            device=global_server_args_dict["device"],
         )
         self.block_size = block_size
         self.scale_fmt = scale_fmt

python/sglang/srt/layers/attention/triton_ops/double_sparsity_attention.py

@@ -2,7 +2,7 @@ import torch
 import triton
 import triton.language as tl
 
-from sglang.srt.server_args import get_global_server_args
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import is_cuda, is_hip
 
 _is_cuda = is_cuda()
@@ -11,7 +11,7 @@ if _is_cuda:
 
 _is_hip = is_hip()
 
-if get_global_server_args().triton_attention_reduce_in_fp32:
+if global_server_args_dict.get("attention_reduce_in_fp32", False):
     REDUCE_TRITON_TYPE = tl.float32
     REDUCE_TORCH_TYPE = torch.float32
 else:

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

@@ -20,8 +20,8 @@ from sglang.srt.layers.attention.utils import (
     create_flashmla_kv_indices_triton,
 )
 from sglang.srt.layers.dp_attention import get_attention_tp_size
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import is_cuda, is_flashinfer_available
 
 if is_flashinfer_available():
@@ -123,9 +123,9 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
         self.forward_prefill_metadata: Optional[TRTLLMMLAPrefillMetadata] = None
         self.forward_decode_metadata: Union[TRTLLMMLADecodeMetadata, None] = None
 
-        self.disable_chunked_prefix_cache = (
-            get_global_server_args().disable_chunked_prefix_cache
-        )
+        self.disable_chunked_prefix_cache = global_server_args_dict[
+            "disable_chunked_prefix_cache"
+        ]
 
         self.num_draft_tokens = model_runner.server_args.speculative_num_draft_tokens
 

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

@@ -45,7 +45,7 @@ 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.server_args import get_global_server_args
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import add_prefix
 
 ROTARY_EMBED_CLASSES = {
@@ -468,7 +468,7 @@ class VisionAttention(nn.Module):
         _passed_backend = qkv_backend
         qkv_backend = self._determine_attention_backend(_passed_backend)
         if (
-            get_global_server_args().mm_attention_backend is None
+            global_server_args_dict["mm_attention_backend"] is None
             and _passed_backend is None
         ):
             print_info_once(f"Multimodal attention backend not set. Use {qkv_backend}.")
@@ -528,7 +528,7 @@ class VisionAttention(nn.Module):
         - CUDA: "triton_attn"
         - Non-CUDA: "sdpa"
         """
-        override_backend = get_global_server_args().mm_attention_backend
+        override_backend = global_server_args_dict["mm_attention_backend"]
         if override_backend is not None:
             backend = override_backend
         elif passed_backend is not None:

python/sglang/srt/layers/communicator.py

@@ -40,9 +40,8 @@ from sglang.srt.layers.moe import (
     get_moe_a2a_backend,
     should_use_flashinfer_cutlass_moe_fp4_allgather,
 )
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.server_args import get_global_server_args
-from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
     get_bool_env_var,
     is_cuda,
@@ -169,7 +168,7 @@ class LayerScatterModes:
 
 
 def enable_moe_dense_fully_dp():
-    return get_global_server_args().moe_dense_tp_size == 1
+    return global_server_args_dict["moe_dense_tp_size"] == 1
 
 
 class LayerCommunicator:
@@ -315,9 +314,7 @@ class LayerCommunicator:
     def should_fuse_mlp_allreduce_with_next_layer(
         self, forward_batch: ForwardBatch
     ) -> bool:
-        speculative_algo = SpeculativeAlgorithm.from_string(
-            get_global_server_args().speculative_algorithm
-        )
+        speculative_algo = global_server_args_dict.get("speculative_algorithm", None)
         if (
             is_dp_attention_enabled()
             and speculative_algo is not None
@@ -336,7 +333,7 @@ class LayerCommunicator:
         static_conditions_met = (
             (not self.is_last_layer)
             and (self._context.tp_size > 1)
-            and get_global_server_args().enable_flashinfer_allreduce_fusion
+            and global_server_args_dict.get("enable_flashinfer_allreduce_fusion", False)
             and _is_flashinfer_available
         )
 
@@ -534,7 +531,7 @@ class CommunicateWithAllReduceAndLayerNormFn:
                 (_is_sm100_supported or _is_sm90_supported)
                 and _is_flashinfer_available
                 and hasattr(layernorm, "forward_with_allreduce_fusion")
-                and get_global_server_args().enable_flashinfer_allreduce_fusion
+                and global_server_args_dict["enable_flashinfer_allreduce_fusion"]
                 and hidden_states.shape[0] <= 4096
             ):
                 hidden_states, residual = layernorm.forward_with_allreduce_fusion(

python/sglang/srt/layers/logits_processor.py

@@ -38,15 +38,17 @@ from sglang.srt.layers.dp_attention import (
     get_dp_device,
     get_dp_dtype,
     get_dp_hidden_size,
+    get_global_dp_buffer,
     get_local_attention_dp_size,
+    set_dp_buffer_len,
 )
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
     ForwardBatch,
     ForwardMode,
 )
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import dump_to_file, is_npu, use_intel_amx_backend
 
 logger = logging.getLogger(__name__)
@@ -228,8 +230,8 @@ class LogitsProcessor(nn.Module):
         super().__init__()
         self.config = config
         self.logit_scale = logit_scale
-        self.use_attn_tp_group = get_global_server_args().enable_dp_lm_head
-        self.use_fp32_lm_head = get_global_server_args().enable_fp32_lm_head
+        self.use_attn_tp_group = global_server_args_dict["enable_dp_lm_head"]
+        self.use_fp32_lm_head = global_server_args_dict["enable_fp32_lm_head"]
         if self.use_attn_tp_group:
             self.attn_tp_size = get_attention_tp_size()
             self.do_tensor_parallel_all_gather = (
@@ -252,8 +254,8 @@ class LogitsProcessor(nn.Module):
         ):
             self.final_logit_softcapping = None
 
-        self.debug_tensor_dump_output_folder = (
-            get_global_server_args().debug_tensor_dump_output_folder
+        self.debug_tensor_dump_output_folder = global_server_args_dict.get(
+            "debug_tensor_dump_output_folder", None
         )
 
     def compute_logprobs_for_multi_item_scoring(
@@ -370,7 +372,9 @@ class LogitsProcessor(nn.Module):
             logits_metadata = LogitsMetadata.from_forward_batch(logits_metadata)
 
         # Check if multi-item scoring is enabled via server args (only for prefill-only requests)
-        multi_item_delimiter = get_global_server_args().multi_item_scoring_delimiter
+        multi_item_delimiter = global_server_args_dict.get(
+            "multi_item_scoring_delimiter"
+        )
         if multi_item_delimiter is not None and logits_metadata.is_prefill_only:
             return self.compute_logprobs_for_multi_item_scoring(
                 input_ids, hidden_states, lm_head, logits_metadata, multi_item_delimiter

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -27,10 +27,12 @@ from sglang.srt.layers.moe.topk import TopKOutput, TopKOutputChecker
 from sglang.srt.layers.quantization.base_config import (
     FusedMoEMethodBase,
     QuantizationConfig,
+    QuantizeMethodBase,
 )
 from sglang.srt.layers.quantization.fp8 import Fp8MoEMethod
 from sglang.srt.layers.quantization.modelopt_quant import ModelOptNvFp4FusedMoEMethod
 from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
 from sglang.srt.utils import (
     cpu_has_amx_support,

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

@@ -31,7 +31,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizeMethodBase,
 )
 from sglang.srt.layers.quantization.utils import is_layer_skipped
-from sglang.srt.server_args import get_global_server_args
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import (
     direct_register_custom_op,
     is_cuda,
@@ -265,9 +265,9 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.use_triton_kernels = get_moe_runner_backend().is_triton_kernel()
         self.with_bias = False
         self.use_flashinfer = get_moe_runner_backend().is_flashinfer_mxfp4()
-        self.flashinfer_mxfp4_moe_precision = (
-            get_global_server_args().flashinfer_mxfp4_moe_precision
-        )
+        self.flashinfer_mxfp4_moe_precision = global_server_args_dict[
+            "flashinfer_mxfp4_moe_precision"
+        ]
 
         self.triton_kernel_moe_forward = None
         self.triton_kernel_moe_with_bias_forward = None

python/sglang/srt/layers/sampler.py

@@ -11,8 +11,8 @@ from sglang.srt.layers.dp_attention import (
     is_dp_attention_enabled,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import crash_on_warnings, get_bool_env_var, is_cuda
 
 if is_cuda():
@@ -33,7 +33,7 @@ RETURN_ORIGINAL_LOGPROB = get_bool_env_var("RETURN_ORIGINAL_LOGPROB")
 class Sampler(nn.Module):
     def __init__(self):
         super().__init__()
-        self.use_nan_detection = get_global_server_args().enable_nan_detection
+        self.use_nan_detection = global_server_args_dict["enable_nan_detection"]
         self.tp_sync_group = get_tp_group().device_group
 
         if is_dp_attention_enabled():
@@ -103,7 +103,7 @@ class Sampler(nn.Module):
             del logits
 
             if True:  # Keep this redundant check to simplify some internal code sync
-                if get_global_server_args().sampling_backend == "flashinfer":
+                if global_server_args_dict["sampling_backend"] == "flashinfer":
                     if sampling_info.need_min_p_sampling:
                         probs = top_k_renorm_prob(probs, sampling_info.top_ks)
                         probs = top_p_renorm_prob(probs, sampling_info.top_ps)
@@ -118,7 +118,7 @@ class Sampler(nn.Module):
                             filter_apply_order="joint",
                             check_nan=self.use_nan_detection,
                         )
-                elif get_global_server_args().sampling_backend == "pytorch":
+                elif global_server_args_dict["sampling_backend"] == "pytorch":
                     # A slower fallback implementation with torch native operations.
                     batch_next_token_ids = top_k_top_p_min_p_sampling_from_probs_torch(
                         probs,
@@ -131,7 +131,7 @@ class Sampler(nn.Module):
                     )
                 else:
                     raise ValueError(
-                        f"Invalid sampling backend: {get_global_server_args().sampling_backend}"
+                        f"Invalid sampling backend: {global_server_args_dict['sampling_backend']}"
                     )
 
             if return_logprob: