Clean up server args (#10770)

python/sglang/srt/server_args.py

@@ -19,8 +19,6 @@ import json
 import logging
 import os
 import random
-import socket
-import sys
 import tempfile
 from typing import List, Literal, Optional, Union
 
@@ -328,6 +326,10 @@ class ServerArgs:
     deepep_config: Optional[str] = None
     moe_dense_tp_size: Optional[int] = None
 
+    # Mamba cache
+    max_mamba_cache_size: Optional[int] = None
+    mamba_ssm_dtype: str = "float32"
+
     # Hierarchical cache
     enable_hierarchical_cache: bool = False
     hicache_ratio: float = 2.0
@@ -398,6 +400,7 @@ class ServerArgs:
     enable_return_hidden_states: bool = False
     scheduler_recv_interval: int = 1
     numa_node: Optional[List[int]] = None
+    enable_deterministic_inference: bool = False
 
     # Dynamic batch tokenizer
     enable_dynamic_batch_tokenizer: bool = False
@@ -419,15 +422,12 @@ class ServerArgs:
     disaggregation_prefill_pp: Optional[int] = 1
     disaggregation_ib_device: Optional[str] = None
     num_reserved_decode_tokens: int = 512  # used for decode kv cache offload in PD
-
     # FIXME: hack to reduce ITL when decode bs is small
     disaggregation_decode_polling_interval: int = 1
 
-    # For model weight update
+    # For model weight update and weight loading
     custom_weight_loader: Optional[List[str]] = None
     weight_loader_disable_mmap: bool = False
-
-    # Remote instance weight loading
     remote_instance_weight_loader_seed_instance_ip: Optional[str] = None
     remote_instance_weight_loader_seed_instance_service_port: Optional[int] = None
     remote_instance_weight_loader_send_weights_group_ports: Optional[List[int]] = None
@@ -436,58 +436,84 @@ class ServerArgs:
     enable_pdmux: bool = False
     sm_group_num: int = 3
 
-    # Mamba cache
-    max_mamba_cache_size: Optional[int] = None
-    mamba_ssm_dtype: str = "float32"
+    def __post_init__(self):
+        """
+        Orchestrates the handling of various server arguments, ensuring proper configuration and validation.
+        """
+        # Handle deprecated arguments.
+        self._handle_deprecated_args()
 
-    # For deterministic inference
-    enable_deterministic_inference: bool = False
+        # Set missing default values.
+        self._handle_missing_default_values()
+
+        # Get GPU memory capacity, which is a common dependency for several configuration steps.
+        gpu_mem = get_device_memory_capacity(self.device)
+
+        # Handle memory-related configurations.
+        self._handle_mem_fraction_static(gpu_mem)
+        self._handle_chunked_prefill_size(gpu_mem)
+
+        # Handle CUDA graph settings.
+        self._handle_cuda_graph_max_bs(gpu_mem)
+
+        # Handle device-specific backends.
+        self._handle_hpu_backends()
+        self._handle_cpu_backends()
+
+        # Apply model-specific adjustments.
+        self._handle_model_specific_adjustments()
+
+        # Set kernel backends.
+        self._handle_sampling_backend()
+        self._handle_attention_backend_compatibility()
+        self._handle_page_size()
+        self._handle_amd_specifics()
+        self._handle_grammar_backend()
+
+        # Handle data parallelism.
+        self._handle_data_parallelism()
+
+        # Handle MoE configurations.
+        self._handle_moe_kernel_config()
+        self._handle_deepep_moe()
+        self._handle_eplb_and_dispatch()
+        self._handle_expert_distribution_metrics()
+
+        # Handle pipeline parallelism.
+        self._handle_pipeline_parallelism()
+
+        # Handle Hicache settings.
+        self._handle_hicache()
+
+        # Handle speculative decoding logic.
+        self._handle_speculative_decoding()
+
+        # Handle model loading format.
+        self._handle_load_format()
+
+        # Handle PD disaggregation.
+        self._handle_disaggregation()
+
+        # Validate tokenizer settings.
+        self._handle_tokenizer_batching()
+
+        # Propagate environment variables.
+        self._handle_environment_variables()
+
+        # Validate cache settings.
+        self._handle_cache_compatibility()
+
+        # Validate metrics labels.
+        self._handle_metrics_labels()
 
-    # Deprecated arguments
-    enable_ep_moe: bool = False
-    enable_deepep_moe: bool = False
-    enable_flashinfer_cutlass_moe: bool = False
-    enable_flashinfer_cutedsl_moe: bool = False
-    enable_flashinfer_trtllm_moe: bool = False
-    enable_triton_kernel_moe: bool = False
-    enable_flashinfer_mxfp4_moe: bool = False
+        # Handle deterministic inference.
+        self._handle_deterministic_inference()
+
+        # Handle any other necessary validations.
+        self._handle_other_validations()
 
     def _handle_deprecated_args(self):
-        if self.enable_ep_moe:
-            self.ep_size = self.tp_size
-            print_deprecated_warning(
-                "NOTE: --enable-ep-moe is deprecated. Please set `--ep-size` to the same value as `--tp-size` instead."
-            )
-        if self.enable_deepep_moe:
-            self.moe_a2a_backend = "deepep"
-            print_deprecated_warning(
-                "NOTE: --enable-deepep-moe is deprecated. Please set `--moe-a2a-backend` to 'deepep' instead."
-            )
-        if self.enable_triton_kernel_moe:
-            self.moe_runner_backend = "triton_kernel"
-            print_deprecated_warning(
-                "NOTE: --enable-triton-kernel-moe is deprecated. Please set `--moe-runner-backend` to 'triton_kernel' instead."
-            )
-        if self.enable_flashinfer_cutedsl_moe:
-            self.moe_runner_backend = "flashinfer_cutedsl"
-            print_deprecated_warning(
-                "NOTE: --enable-flashinfer-cutedsl-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_cutedsl' instead."
-            )
-        if self.enable_flashinfer_cutlass_moe:
-            self.moe_runner_backend = "flashinfer_cutlass"
-            print_deprecated_warning(
-                "NOTE: --enable-flashinfer-cutlass-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_cutlass' instead."
-            )
-        if self.enable_flashinfer_trtllm_moe:
-            self.moe_runner_backend = "flashinfer_trtllm"
-            print_deprecated_warning(
-                "NOTE: --enable-flashinfer-trtllm-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_trtllm' instead."
-            )
-        if self.enable_flashinfer_mxfp4_moe:
-            self.moe_runner_backend = "flashinfer_mxfp4"
-            print_deprecated_warning(
-                "NOTE: --enable-flashinfer-mxfp4-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_mxfp4' instead."
-            )
+        pass
 
     def _handle_missing_default_values(self):
         if self.tokenizer_path is None:
@@ -590,6 +616,84 @@ class ServerArgs:
                 self.attention_backend = "intel_amx"
             self.sampling_backend = "pytorch"
 
+    def _handle_model_specific_adjustments(self):
+        if parse_connector_type(self.model_path) == ConnectorType.INSTANCE:
+            return
+
+        hf_config = self.get_hf_config()
+        model_arch = hf_config.architectures[0]
+        if model_arch in ["GptOssForCausalLM"]:
+            if self.attention_backend is None:
+                if is_cuda() and is_sm100_supported():
+                    self.attention_backend = "trtllm_mha"
+                elif is_cuda() and is_sm90_supported():
+                    self.attention_backend = "fa3"
+                else:
+                    self.attention_backend = "triton"
+            supported_backends = ["triton", "trtllm_mha", "fa3"]
+            logger.info(
+                f"Use {self.attention_backend} as attention backend for GptOssForCausalLM"
+            )
+            assert (
+                self.attention_backend in supported_backends
+            ), f"GptOssForCausalLM requires one of {supported_backends} attention backend, but got '{self.attention_backend}'"
+
+            if is_sm100_supported():
+                if not self.enable_dp_attention:
+                    self.enable_flashinfer_allreduce_fusion = True
+                    logger.info(
+                        "Enable FlashInfer AllReduce Fusion on sm100 for GptOssForCausalLM"
+                    )
+            quantization_config = getattr(hf_config, "quantization_config", None)
+            is_mxfp4_quant_format = (
+                quantization_config is not None
+                and quantization_config.get("quant_method") == "mxfp4"
+            )
+
+            if is_sm100_supported() and is_mxfp4_quant_format:
+                self.moe_runner_backend = "flashinfer_mxfp4"
+                logger.warning(
+                    "Detected SM100 and MXFP4 quantization format for GPT-OSS model, enabling FlashInfer MXFP4 MOE kernel."
+                )
+            else:
+                if self.moe_runner_backend == "triton_kernel":
+                    assert (
+                        self.ep_size == 1
+                    ), "Triton kernel MoE is only supported when ep_size == 1"
+                if (
+                    self.moe_runner_backend == "auto"
+                    and self.ep_size == 1
+                    and is_triton_kernels_available()
+                ):
+                    self.moe_runner_backend = "triton_kernel"
+                    logger.warning(
+                        "Detected GPT-OSS model, enabling triton_kernels MOE kernel."
+                    )
+            self.disable_hybrid_swa_memory = True
+            if is_mxfp4_quant_format:
+                # use bf16 for mxfp4 triton kernels
+                self.dtype = "bfloat16"
+
+        elif "Llama4" in model_arch and self.device != "cpu":
+            assert self.attention_backend in {
+                "fa3",
+                "aiter",
+                "triton",
+            }, "fa3, aiter, or triton is required for Llama4 model"
+        elif model_arch in [
+            "Gemma2ForCausalLM",
+            "Gemma3ForCausalLM",
+            "Gemma3ForConditionalGeneration",
+            "Gemma3nForCausalLM",
+            "Gemma3nForConditionalGeneration",
+        ]:
+            # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with gemma2 model.
+            # It failed at this test: https://github.com/sgl-project/sglang/actions/runs/16255155597/job/45890331952#step:4:736
+            logger.warning(
+                f"Disable hybrid SWA memory for {model_arch} as it is not yet supported."
+            )
+            self.disable_hybrid_swa_memory = True
+
     def _handle_sampling_backend(self):
         if self.sampling_backend is None:
             self.sampling_backend = (
@@ -1014,83 +1118,6 @@ class ServerArgs:
     def _handle_other_validations(self):
         pass
 
-    def __post_init__(self):
-        """
-        Orchestrates the handling of various server arguments, ensuring proper configuration and validation.
-        """
-        # Step 1: Handle deprecated arguments.
-        self._handle_deprecated_args()
-
-        # Step 2: Set missing default values.
-        self._handle_missing_default_values()
-
-        # Get GPU memory capacity, which is a common dependency for several configuration steps.
-        gpu_mem = get_device_memory_capacity(self.device)
-
-        # Step 3: Handle memory-related configurations.
-        self._handle_mem_fraction_static(gpu_mem)
-        self._handle_chunked_prefill_size(gpu_mem)
-
-        # Step 4: Handle CUDA graph settings.
-        self._handle_cuda_graph_max_bs(gpu_mem)
-
-        # Step 5: Handle device-specific backends.
-        self._handle_hpu_backends()
-        self._handle_cpu_backends()
-
-        # Step 6: Apply model-specific adjustments.
-        if parse_connector_type(self.model_path) != ConnectorType.INSTANCE:
-            self.model_specific_adjustments()
-
-        # Step 7: Set kernel backends.
-        self._handle_sampling_backend()
-        self._handle_attention_backend_compatibility()
-        self._handle_page_size()
-        self._handle_amd_specifics()
-        self._handle_grammar_backend()
-
-        # Step 8: Handle data parallelism.
-        self._handle_data_parallelism()
-
-        # Step 9: Handle MoE configurations.
-        self._handle_moe_kernel_config()
-        self._handle_deepep_moe()
-        self._handle_eplb_and_dispatch()
-        self._handle_expert_distribution_metrics()
-
-        # Step 10: Handle pipeline parallelism.
-        self._handle_pipeline_parallelism()
-
-        # Step 11: Handle Hicache settings.
-        self._handle_hicache()
-
-        # Step 12: Handle speculative decoding logic.
-        self._handle_speculative_decoding()
-
-        # Step 13: Handle model loading format.
-        self._handle_load_format()
-
-        # Step 14: Handle PD disaggregation.
-        self._handle_disaggregation()
-
-        # Step 15: Validate tokenizer settings.
-        self._handle_tokenizer_batching()
-
-        # Step 16: Propagate environment variables.
-        self._handle_environment_variables()
-
-        # Step 17: Validate cache settings.
-        self._handle_cache_compatibility()
-
-        # Step 18: Validate metrics labels.
-        self._handle_metrics_labels()
-
-        # Step 19: Handle deterministic inference.
-        self._handle_deterministic_inference()
-
-        # Step 20: Handle any other necessary validations.
-        self._handle_other_validations()
-
     @staticmethod
     def add_cli_args(parser: argparse.ArgumentParser):
         # Model and tokenizer
@@ -1101,24 +1128,6 @@ class ServerArgs:
             help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
             required=True,
         )
-        parser.add_argument(
-            "--remote-instance-weight-loader-seed-instance-ip",
-            type=str,
-            default=ServerArgs.remote_instance_weight_loader_seed_instance_ip,
-            help="The ip of the seed instance for loading weights from remote instance.",
-        )
-        parser.add_argument(
-            "--remote-instance-weight-loader-seed-instance-service-port",
-            type=int,
-            default=ServerArgs.remote_instance_weight_loader_seed_instance_service_port,
-            help="The service port of the seed instance for loading weights from remote instance.",
-        )
-        parser.add_argument(
-            "--remote-instance-weight-loader-send-weights-group-ports",
-            type=json_list_type,
-            default=ServerArgs.remote_instance_weight_loader_send_weights_group_ports,
-            help="The communication group ports for loading weights from remote instance.",
-        )
         parser.add_argument(
             "--tokenizer-path",
             type=str,
@@ -2573,6 +2582,24 @@ class ServerArgs:
             action="store_true",
             help="Disable mmap while loading weight using safetensors.",
         )
+        parser.add_argument(
+            "--remote-instance-weight-loader-seed-instance-ip",
+            type=str,
+            default=ServerArgs.remote_instance_weight_loader_seed_instance_ip,
+            help="The ip of the seed instance for loading weights from remote instance.",
+        )
+        parser.add_argument(
+            "--remote-instance-weight-loader-seed-instance-service-port",
+            type=int,
+            default=ServerArgs.remote_instance_weight_loader_seed_instance_service_port,
+            help="The service port of the seed instance for loading weights from remote instance.",
+        )
+        parser.add_argument(
+            "--remote-instance-weight-loader-send-weights-group-ports",
+            type=json_list_type,
+            default=ServerArgs.remote_instance_weight_loader_send_weights_group_ports,
+            help="The communication group ports for loading weights from remote instance.",
+        )
 
         # For PD-Multiplexing
         parser.add_argument(
@@ -2598,38 +2625,38 @@ class ServerArgs:
         # Deprecated arguments
         parser.add_argument(
             "--enable-ep-moe",
-            action="store_true",
-            help="(Deprecated) Enabling expert parallelism for moe. The ep size is equal to the tp size.",
+            action=DeprecatedAction,
+            help="NOTE: --enable-ep-moe is deprecated. Please set `--ep-size` to the same value as `--tp-size` instead.",
         )
         parser.add_argument(
             "--enable-deepep-moe",
-            action="store_true",
-            help="(Deprecated) Enabling DeepEP MoE implementation for EP MoE.",
+            action=DeprecatedAction,
+            help="NOTE: --enable-deepep-moe is deprecated. Please set `--moe-a2a-backend` to 'deepep' instead.",
         )
         parser.add_argument(
             "--enable-flashinfer-cutlass-moe",
-            action="store_true",
-            help="(Deprecated) Enable FlashInfer CUTLASS MoE backend for modelopt_fp4 quant on Blackwell. Supports MoE-EP",
+            action=DeprecatedAction,
+            help="NOTE: --enable-flashinfer-cutlass-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_cutlass' instead.",
         )
         parser.add_argument(
             "--enable-flashinfer-cutedsl-moe",
-            action="store_true",
-            help="(Deprecated) Enable FlashInfer CuteDSL MoE backend for modelopt_fp4 quant on Blackwell. Supports MoE-EP",
+            action=DeprecatedAction,
+            help="NOTE: --enable-flashinfer-cutedsl-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_cutedsl' instead.",
         )
         parser.add_argument(
             "--enable-flashinfer-trtllm-moe",
-            action="store_true",
-            help="(Deprecated) Enable FlashInfer TRTLLM MoE backend on Blackwell. Supports BlockScale FP8 MoE-EP",
+            action=DeprecatedAction,
+            help="NOTE: --enable-flashinfer-trtllm-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_trtllm' instead.",
         )
         parser.add_argument(
             "--enable-triton-kernel-moe",
-            action="store_true",
-            help="(Deprecated) Use triton moe grouped gemm kernel.",
+            action=DeprecatedAction,
+            help="NOTE: --enable-triton-kernel-moe is deprecated. Please set `--moe-runner-backend` to 'triton_kernel' instead.",
         )
         parser.add_argument(
             "--enable-flashinfer-mxfp4-moe",
-            action="store_true",
-            help="(Deprecated) Enable FlashInfer MXFP4 MoE backend for modelopt_fp4 quant on Blackwell.",
+            action=DeprecatedAction,
+            help="NOTE: --enable-flashinfer-mxfp4-moe is deprecated. Please set `--moe-runner-backend` to 'flashinfer_mxfp4' instead.",
         )
 
     @classmethod
@@ -2862,81 +2889,6 @@ class ServerArgs:
                 val >= 0 for val in bucket_values
             ), f"{arg_name} customer rule bucket values should be non-negative"
 
-    def model_specific_adjustments(self):
-        hf_config = self.get_hf_config()
-        model_arch = hf_config.architectures[0]
-        if model_arch in ["GptOssForCausalLM"]:
-            if self.attention_backend is None:
-                if is_cuda() and is_sm100_supported():
-                    self.attention_backend = "trtllm_mha"
-                elif is_cuda() and is_sm90_supported():
-                    self.attention_backend = "fa3"
-                else:
-                    self.attention_backend = "triton"
-            supported_backends = ["triton", "trtllm_mha", "fa3"]
-            logger.info(
-                f"Use {self.attention_backend} as attention backend for GptOssForCausalLM"
-            )
-            assert (
-                self.attention_backend in supported_backends
-            ), f"GptOssForCausalLM requires one of {supported_backends} attention backend, but got '{self.attention_backend}'"
-
-            if is_sm100_supported():
-                if not self.enable_dp_attention:
-                    self.enable_flashinfer_allreduce_fusion = True
-                    logger.info(
-                        "Enable FlashInfer AllReduce Fusion on sm100 for GptOssForCausalLM"
-                    )
-            quantization_config = getattr(hf_config, "quantization_config", None)
-            is_mxfp4_quant_format = (
-                quantization_config is not None
-                and quantization_config.get("quant_method") == "mxfp4"
-            )
-
-            if is_sm100_supported() and is_mxfp4_quant_format:
-                self.moe_runner_backend = "flashinfer_mxfp4"
-                logger.warning(
-                    "Detected SM100 and MXFP4 quantization format for GPT-OSS model, enabling FlashInfer MXFP4 MOE kernel."
-                )
-            else:
-                if self.moe_runner_backend == "triton_kernel":
-                    assert (
-                        self.ep_size == 1
-                    ), "Triton kernel MoE is only supported when ep_size == 1"
-                if (
-                    self.moe_runner_backend == "auto"
-                    and self.ep_size == 1
-                    and is_triton_kernels_available()
-                ):
-                    self.moe_runner_backend = "triton_kernel"
-                    logger.warning(
-                        "Detected GPT-OSS model, enabling triton_kernels MOE kernel."
-                    )
-            self.disable_hybrid_swa_memory = True
-            if is_mxfp4_quant_format:
-                # use bf16 for mxfp4 triton kernels
-                self.dtype = "bfloat16"
-
-        elif "Llama4" in model_arch and self.device != "cpu":
-            assert self.attention_backend in {
-                "fa3",
-                "aiter",
-                "triton",
-            }, "fa3, aiter, or triton is required for Llama4 model"
-        elif model_arch in [
-            "Gemma2ForCausalLM",
-            "Gemma3ForCausalLM",
-            "Gemma3ForConditionalGeneration",
-            "Gemma3nForCausalLM",
-            "Gemma3nForConditionalGeneration",
-        ]:
-            # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with gemma2 model.
-            # It failed at this test: https://github.com/sgl-project/sglang/actions/runs/16255155597/job/45890331952#step:4:736
-            logger.warning(
-                f"Disable hybrid SWA memory for {model_arch} as it is not yet supported."
-            )
-            self.disable_hybrid_swa_memory = True
-
     def adjust_mem_fraction_for_vlm(self, model_config):
         vision_config = getattr(model_config.hf_config, "vision_config", None)
         if vision_config is None: