use envs in server_args (#22994)

python/sglang/srt/environ.py

@@ -315,8 +315,10 @@ class Envs:
 
     # AMD & ROCm
     SGLANG_USE_AITER = EnvBool(False)
+    SGLANG_USE_AITER_UNIFIED_ATTN = EnvBool(False)
     SGLANG_ROCM_FUSED_DECODE_MLA = EnvBool(False)
     SGLANG_ROCM_DISABLE_LINEARQUANT = EnvBool(False)
+    SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK = EnvInt(4096)
 
     # MPS (Apple Silicon)
     SGLANG_USE_MLX = EnvBool(False)

python/sglang/srt/server_args.py

@@ -37,12 +37,10 @@ from sglang.srt.utils.common import (
     LORA_TARGET_ALL_MODULES,
     SUPPORTED_LORA_TARGET_MODULES,
     cpu_has_amx_support,
-    get_bool_env_var,
     get_device,
     get_device_memory_capacity,
     get_device_name,
     get_device_sm,
-    get_int_env_var,
     get_nvidia_driver_version,
     get_quantization_config,
     human_readable_int,
@@ -1025,7 +1023,7 @@ class ServerArgs:
             self.mm_process_config = {}
 
         # Handle ModelScope model downloads
-        if get_bool_env_var("SGLANG_USE_MODELSCOPE"):
+        if envs.SGLANG_USE_MODELSCOPE.get():
             self._handle_modelscope_paths()
 
         # Mamba scheduler strategy
@@ -1851,13 +1849,13 @@ class ServerArgs:
                         "Detected SM120 and MXFP4 quantization format for GPT-OSS model, enabling triton_kernel MOE kernel."
                     )
                 elif (
-                    is_hip() and get_bool_env_var("SGLANG_USE_AITER")
+                    is_hip() and envs.SGLANG_USE_AITER.get()
                 ) and is_mxfp4_quant_format:
                     self.moe_runner_backend = "auto"
                     logger.warning(
                         "Detected ROCm and MXFP4 quantization format for GPT-OSS model, enabling aiter MXFP4 MOE kernel."
                     )
-                elif is_hip() and get_bool_env_var("SGLANG_USE_AITER"):
+                elif is_hip() and envs.SGLANG_USE_AITER.get():
                     # For GPT-OSS bf16 on ROCm with aiter, use triton backend
                     # because aiter CK kernel doesn't support all GEMM dimensions
                     self.moe_runner_backend = "triton"
@@ -2175,7 +2173,7 @@ class ServerArgs:
         if (
             model_arch in ["Qwen3VLForConditionalGeneration"]
             and is_hip()
-            and get_bool_env_var("SGLANG_USE_AITER_UNIFIED_ATTN")
+            and envs.SGLANG_USE_AITER_UNIFIED_ATTN.get()
             and self.page_size is None
         ):
             self.page_size = 16
@@ -2851,7 +2849,7 @@ class ServerArgs:
                 "FlashInfer TRTLLM routed MoE is enabled. --disable-shared-experts-fusion is automatically set."
             )
 
-        if get_bool_env_var("SGLANG_CUTLASS_MOE"):
+        if envs.SGLANG_CUTLASS_MOE.get():
             logger.warning(
                 "SGLANG_CUTLASS_MOE is deprecated, use --moe-runner-backend=cutlass and/or --speculative-moe-runner-backend=cutlass instead"
             )
@@ -2911,12 +2909,12 @@ class ServerArgs:
             logger.warning(
                 f"Ascend fused EP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
             )
-            fuse_mode = os.environ.get("SGLANG_NPU_FUSED_MOE_MODE", None)
+            fuse_mode = envs.SGLANG_NPU_FUSED_MOE_MODE.get()
-            if fuse_mode not in ["1", "2"]:
+            if fuse_mode not in [1, 2]:
                 raise ValueError(
                     f"Wrong value of {fuse_mode=}, the NPU only support 1 or 2."
                 )
-            elif fuse_mode == "2":
+            elif fuse_mode == 2:
                 assert (
                     self.quantization == "modelslim"
                 ), "When fuse_mode is set to 2, the NPU supports only ModelSlim quantization."
@@ -2931,7 +2929,7 @@ class ServerArgs:
             )
             if self.deepep_mode != "auto":
                 logger.warning("--deepep-mode is ignored for Flashinfer MoE A2A")
-            if os.environ.get("SGLANG_MOE_NVFP4_DISPATCH") is None:
+            if not envs.SGLANG_MOE_NVFP4_DISPATCH.is_set():
                 envs.SGLANG_MOE_NVFP4_DISPATCH.set(True)
                 logger.warning(
                     "SGLANG_MOE_NVFP4_DISPATCH is set to True for Flashinfer MoE A2A"
@@ -2953,9 +2951,9 @@ class ServerArgs:
             # Skip validation if chunked prefill is disabled (i.e., size <= 0).
             # Skip validation if disaggregation mode is decode.
             if self.chunked_prefill_size > 0 and self.disaggregation_mode != "decode":
-                assert (self.chunked_prefill_size) <= get_int_env_var(
+                assert (
-                    "SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK", 4096
+                    self.chunked_prefill_size
-                ), "SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK (default 4096) must be larger or equal to chunked_prefill_size"
+                ) <= envs.SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK.get(), "SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK (default 4096) must be larger or equal to chunked_prefill_size"
 
     def _handle_eplb_and_dispatch(self):
         if self.enable_eplb and (self.expert_distribution_recorder_mode is None):
@@ -3739,9 +3737,9 @@ class ServerArgs:
             self.enable_deterministic_inference = True
 
             # For VLM
-            os.environ["SGLANG_VLM_CACHE_SIZE_MB"] = "0"
+            envs.SGLANG_VLM_CACHE_SIZE_MB.set(0)
             # TODO remove this environment variable as a whole
-            os.environ["SGLANG_ENABLE_DETERMINISTIC_INFERENCE"] = "1"
+            envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.set(True)
 
         if self.enable_deterministic_inference:
             if self.enable_aiter_allreduce_fusion: