[Spec] Remove the ServerArgs clone + global save/restore hack from DFlashWorkerV2 (#29995)

python/sglang/srt/arg_groups/speculative_hook.py

@@ -229,6 +229,8 @@ def _handle_dflash(server_args: ServerArgs) -> None:
                 f"window_size={server_args.speculative_draft_window_size}, block_size={draft_tokens}."
             )
 
+    _resolve_dflash_draft_attention_backend(server_args)
+
     if server_args.max_running_requests is None:
         server_args.max_running_requests = 48
         logger.warning(
@@ -242,6 +244,45 @@ def _handle_dflash(server_args: ServerArgs) -> None:
         )
 
 
+def _resolve_dflash_draft_attention_backend(server_args: ServerArgs) -> None:
+    """Resolve `speculative_draft_attention_backend` to a final, supported value.
+
+    Consumed by ModelRunner's `is_draft_worker` override (one backend for all
+    draft modes).
+    """
+    from sglang.srt.utils import is_hip
+
+    supported_draft_backends = ("flashinfer", "fa3", "fa4", "triton", "ascend")
+    # Use triton on ROCm (no FlashInfer), flashinfer on CUDA.
+    fallback_backend = "triton" if is_hip() else "flashinfer"
+
+    draft_backend = server_args.speculative_draft_attention_backend
+    if draft_backend is None:
+        draft_backend, _ = server_args.get_attention_backends()
+    if draft_backend is None:
+        draft_backend = fallback_backend
+    elif draft_backend == "trtllm_mha":
+        logger.warning(
+            "DFLASH draft worker does not support 'trtllm_mha' because the "
+            "draft path requires per-layer DFlash attention. Falling back to "
+            "'%s'.",
+            fallback_backend,
+        )
+        draft_backend = fallback_backend
+    elif draft_backend not in supported_draft_backends:
+        logger.warning(
+            "DFLASH draft worker only supports attention_backend in %s for now, "
+            "but got %r. Falling back to '%s'.",
+            supported_draft_backends,
+            draft_backend,
+            fallback_backend,
+        )
+        draft_backend = fallback_backend
+    # FIXME: avoid overriding server args directly; pass the resolved draft
+    # backend to the draft worker explicitly instead.
+    server_args.speculative_draft_attention_backend = draft_backend
+
+
 def _handle_frozen_kv_mtp(server_args: ServerArgs) -> None:
     if server_args.max_running_requests is None:
         server_args.max_running_requests = 48

python/sglang/srt/configs/model_config.py

@@ -483,6 +483,7 @@ class ModelConfig:
         model_path: str = None,
         model_revision: str = None,
         is_draft_model: bool = False,
+        context_length: Optional[int] = None,
         **kwargs,
     ):
         quantization = (
@@ -499,7 +500,11 @@ class ModelConfig:
             model_path=model_path or server_args.model_path,
             trust_remote_code=server_args.trust_remote_code,
             revision=model_revision or server_args.revision,
-            context_length=server_args.context_length,
+            context_length=(
+                context_length
+                if context_length is not None
+                else server_args.context_length
+            ),
             model_override_args=server_args.json_model_override_args,
             is_embedding=server_args.is_embedding,
             enable_multimodal=server_args.enable_multimodal,

python/sglang/srt/managers/tp_worker.py

@@ -241,6 +241,7 @@ class TpModelWorker(BaseTpWorker):
         token_to_kv_pool_allocator: Optional[BaseTokenToKVPoolAllocator] = None,
         memory_pool_config: Optional[MemoryPoolConfig] = None,
         is_multi_layer_eagle: bool = False,
+        context_length: Optional[int] = None,
     ):
         # Parse args
         self.server_args = server_args
@@ -261,6 +262,9 @@ class TpModelWorker(BaseTpWorker):
         self.moe_dp_rank = moe_dp_rank
         # Draft worker: target's resolved MemoryPoolConfig (forwarded to ModelRunner).
         self.memory_pool_config = memory_pool_config
+        # Draft worker: target's effective context length; the draft runs at
+        # absolute target positions. None keeps server_args.context_length.
+        self.context_length = context_length
 
         # MTP model runners
         self.model_runner_list: List[ModelRunner] = []
@@ -273,7 +277,9 @@ class TpModelWorker(BaseTpWorker):
 
         self._init_dllm_algorithm()
 
-        if server_args.skip_tokenizer_init:
+        if server_args.skip_tokenizer_init or self.is_draft_worker:
+            # A draft worker's tokenizer would only duplicate the target's:
+            # tokenizer_path always points at the target model.
             self.tokenizer = self.processor = None
         else:
             if self.model_config.is_multimodal:
@@ -370,6 +376,7 @@ class TpModelWorker(BaseTpWorker):
                 else self.server_args.speculative_draft_model_revision
             ),
             is_draft_model=self.is_draft_worker,
+            context_length=self.context_length,
         )
 
     def _init_model_runner(self):

python/sglang/srt/model_executor/model_runner.py

@@ -528,12 +528,12 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         # Model-specific adjustment
         self.model_specific_adjustment()
 
-        # Set the global server_args in the scheduler process
+        # 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)
-        global_server_args = get_global_server_args()
-
             # FIXME: hacky set `use_mla_backend`
-        global_server_args.use_mla_backend = self.use_mla_backend
+            get_global_server_args().use_mla_backend = self.use_mla_backend
 
         # Init OpenMP threads binding for CPU
         if self.device == "cpu":
@@ -1128,6 +1128,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             )
 
     def model_specific_adjustment(self):
+        if self.is_draft_worker:
+            return
+
         server_args = self.server_args
 
         # HRM-Text needs bidirectional prompt attention (prefill), which only the

python/sglang/srt/speculative/dflash_worker_v2.py

@@ -1,6 +1,5 @@
 import logging
 import math
-from copy import deepcopy
 from typing import List, Optional
 
 import torch
@@ -15,11 +14,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
     compute_position,
 )
-from sglang.srt.server_args import (
+from sglang.srt.server_args import ServerArgs
-    ServerArgs,
-    get_global_server_args,
-    set_global_server_args_for_scheduler,
-)
 from sglang.srt.speculative.base_spec_worker import BaseSpecWorker
 from sglang.srt.speculative.dflash_info import DFlashVerifyInput
 from sglang.srt.speculative.dflash_info_v2 import DFlashDraftInputV2
@@ -134,52 +129,8 @@ class DFlashWorkerV2(BaseSpecWorker):
         self._logged_first_verify = False
 
         # Draft runner (separate KV cache + attention backend).
-        draft_server_args = deepcopy(server_args)
-        draft_server_args.skip_tokenizer_init = True
-        draft_backend = draft_server_args.speculative_draft_attention_backend
-        supported_draft_backends = ("flashinfer", "fa3", "fa4", "triton", "ascend")
-        if draft_backend is None:
-            draft_backend, _ = draft_server_args.get_attention_backends()
-        if draft_backend is None:
-            # Use triton on ROCm (no FlashInfer), flashinfer on CUDA
-            import torch as _torch
-
-            draft_backend = "triton" if _torch.version.hip else "flashinfer"
-        elif draft_backend == "trtllm_mha":
-            import torch as _torch
-
-            _fb = "triton" if _torch.version.hip else "flashinfer"
-            logger.warning(
-                "DFLASH draft worker does not support 'trtllm_mha' because the "
-                "draft path requires per-layer DFlash attention. Falling back to "
-                "'%s'.",
-                _fb,
-            )
-            draft_backend = _fb
-        elif draft_backend not in supported_draft_backends:
-            import torch as _torch
-
-            _fb = "triton" if _torch.version.hip else "flashinfer"
-            logger.warning(
-                "DFLASH draft worker only supports attention_backend in %s for now, "
-                "but got %r. Falling back to '%s'.",
-                supported_draft_backends,
-                draft_backend,
-                _fb,
-            )
-            draft_backend = _fb
-        # Make the draft worker backend explicit and self-contained (no further overrides).
-        draft_server_args.speculative_draft_attention_backend = None
-        draft_server_args.prefill_attention_backend = None
-        draft_server_args.decode_attention_backend = None
-        draft_server_args.attention_backend = draft_backend
-        # Keep draft context length aligned with the target.
-        draft_server_args.context_length = (
-            target_worker.model_runner.model_config.context_len
-        )
-        saved_server_args = get_global_server_args()
         self._draft_worker = TpModelWorker(
-            server_args=draft_server_args,
+            server_args=server_args,
             gpu_id=gpu_id,
             tp_rank=tp_rank,
             moe_ep_rank=moe_ep_rank,
@@ -189,8 +140,8 @@ class DFlashWorkerV2(BaseSpecWorker):
             dp_rank=dp_rank,
             nccl_port=nccl_port,
             is_draft_worker=True,
+            context_length=target_worker.model_runner.model_config.context_len,
         )
-        set_global_server_args_for_scheduler(saved_server_args)
         self.draft_model_runner = self._draft_worker.model_runner
         self._draft_sampler = None
         # Keep the same alias that other spec-v2 workers expose.
@@ -226,7 +177,7 @@ class DFlashWorkerV2(BaseSpecWorker):
         if self.tp_rank == 0:
             logger.info(
                 "Initialized DFLASH draft runner. attention_backend=%s, model=%s, block_size=%s, draft_window_size=%s, compact_cache=%s",
-                getattr(draft_server_args, "attention_backend", None),
+                server_args.speculative_draft_attention_backend,
                 self.draft_model.__class__.__name__,
                 self.block_size,
                 self.draft_window_size,

python/sglang/srt/speculative/draft_utils.py

@@ -1,6 +1,6 @@
 import logging
 
-from sglang.srt.server_args import ServerArgs, get_global_server_args
+from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils.common import is_blackwell, is_hip, is_musa, is_npu
 
 logger = logging.getLogger(__name__)
@@ -118,7 +118,7 @@ class DraftBackendFactory:
         return DeepseekSparseAttnBackend(self.draft_model_runner, skip_prefill=False)
 
     def _create_flashinfer_decode_backend(self):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             from sglang.srt.layers.attention.flashinfer_backend import (
                 FlashInferMultiStepDraftBackend,
             )
@@ -193,7 +193,7 @@ class DraftBackendFactory:
         )
 
     def _create_trtllm_mla_decode_backend(self, backend: str = "trtllm-gen"):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             raise ValueError(
                 "trtllm_mla backend requires MLA model (use_mla_backend=True)."
             )
@@ -213,7 +213,7 @@ class DraftBackendFactory:
         return self._create_trtllm_mla_decode_backend(backend="cute-dsl")
 
     def _create_tokenspeed_mla_decode_backend(self):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             raise ValueError(
                 "tokenspeed_mla backend requires MLA model (use_mla_backend=True)."
             )
@@ -259,7 +259,7 @@ class DraftBackendFactory:
         )
 
     def _create_flashinfer_prefill_backend(self):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             from sglang.srt.layers.attention.flashinfer_backend import (
                 FlashInferAttnBackend,
             )
@@ -307,7 +307,7 @@ class DraftBackendFactory:
         return TRTLLMHAAttnBackend(self.draft_model_runner, skip_prefill=False)
 
     def _create_trtllm_mla_prefill_backend(self):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             raise ValueError(
                 "trtllm_mla backend requires MLA model (use_mla_backend=True)."
             )
@@ -317,7 +317,7 @@ class DraftBackendFactory:
         return TRTLLMMLABackend(self.draft_model_runner, skip_prefill=False)
 
     def _create_tokenspeed_mla_prefill_backend(self):
-        if not get_global_server_args().use_mla_backend:
+        if not self.draft_model_runner.use_mla_backend:
             raise ValueError(
                 "tokenspeed_mla backend requires MLA model (use_mla_backend=True)."
             )