Clean up server_args.py (#7037)

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

@@ -118,7 +118,7 @@ def _compile_warning_1():
     if not _IN_PRECOMPILE_STAGE and _IS_FIRST_RANK_ON_NODE:
         logger.warning(
             "Entering DeepGEMM JIT Pre-Compile session. "
-            "And it may takes a long time(Typically 10-20 mins) "
+            "It may takes a long time (typically 10-20 mins) "
             "if you have not run `sglang.compile_deep_gemm`. "
             "It is recommended to run `sglang.compile_deep_gemm` with same args as `sglang.launch_server`"
             " for pre-compilation to reduce the overhead if you have not run it before. "

python/sglang/srt/managers/schedule_batch.py

@@ -72,32 +72,33 @@ INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
 
 GLOBAL_SERVER_ARGS_KEYS = [
     "attention_backend",
+    "mm_attention_backend",
     "debug_tensor_dump_inject",
     "debug_tensor_dump_output_folder",
     "chunked_prefill_size",
-    "deepep_mode",
     "device",
     "disable_chunked_prefix_cache",
     "disable_radix_cache",
-    "enable_deepep_moe",
     "enable_dp_attention",
     "enable_two_batch_overlap",
     "enable_dp_lm_head",
+    "enable_deepep_moe",
+    "deepep_mode",
     "enable_ep_moe",
+    "moe_dense_tp_size",
+    "ep_dispatch_algorithm",
     "deepep_config",
+    "ep_num_redundant_experts",
     "enable_nan_detection",
     "flashinfer_mla_disable_ragged",
     "max_micro_batch_size",
-    "moe_dense_tp_size",
-    "ep_dispatch_algorithm",
     "disable_shared_experts_fusion",
     "sampling_backend",
     "speculative_accept_threshold_acc",
     "speculative_accept_threshold_single",
     "torchao_config",
     "triton_attention_reduce_in_fp32",
-    "ep_num_redundant_experts",
-    "mm_attention_backend",
+    "num_reserved_decode_tokens",
 ]
 
 # Put some global args for easy access

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -17,12 +17,14 @@ from __future__ import annotations
 
 import bisect
 import inspect
+import logging
 import os
 from contextlib import contextmanager
 from typing import TYPE_CHECKING, Callable, Optional, Union
 
 import torch
 import tqdm
+from torch.profiler import ProfilerActivity, profile
 
 from sglang.srt.custom_op import CustomOp
 from sglang.srt.distributed import get_tensor_model_parallel_rank
@@ -40,11 +42,14 @@ from sglang.srt.model_executor.forward_batch_info import (
 from sglang.srt.patch_torch import monkey_patch_torch_compile
 from sglang.srt.two_batch_overlap import TboCudaGraphRunnerPlugin
 from sglang.srt.utils import (
+    empty_context,
     get_available_gpu_memory,
     get_device_memory_capacity,
     rank0_log,
 )
 
+logger = logging.getLogger(__name__)
+
 if TYPE_CHECKING:
     from sglang.srt.model_executor.model_runner import ModelRunner
 
@@ -207,6 +212,9 @@ class CudaGraphRunner:
             model_runner.server_args.enable_two_batch_overlap
         )
         self.speculative_algorithm = model_runner.server_args.speculative_algorithm
+        self.enable_profile_cuda_graph = (
+            model_runner.server_args.enable_profile_cuda_graph
+        )
         self.tp_size = model_runner.server_args.tp_size
         self.dp_size = model_runner.server_args.dp_size
         self.pp_size = model_runner.server_args.pp_size
@@ -339,44 +347,67 @@ class CudaGraphRunner:
 
         return is_bs_supported and is_encoder_lens_supported and is_tbo_supported
 
-    def capture(self):
-        with graph_capture() as graph_capture_context:
-            self.stream = graph_capture_context.stream
-            avail_mem = get_available_gpu_memory(
-                self.model_runner.device, self.model_runner.gpu_id, empty_cache=False
+    def capture(self) -> None:
+        profile_context = empty_context()
+        if self.enable_profile_cuda_graph:
+            profile_context = profile(
+                activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+                record_shapes=True,
             )
-            # Reverse the order to enable better memory sharing across cuda graphs.
-            capture_range = (
-                tqdm.tqdm(list(reversed(self.capture_bs)))
-                if get_tensor_model_parallel_rank() == 0
-                else reversed(self.capture_bs)
-            )
-            for bs in capture_range:
-                if get_tensor_model_parallel_rank() == 0:
-                    avail_mem = get_available_gpu_memory(
-                        self.model_runner.device,
-                        self.model_runner.gpu_id,
-                        empty_cache=False,
-                    )
-                    capture_range.set_description(
-                        f"Capturing batches ({avail_mem=:.2f} GB)"
-                    )
-
-                with patch_model(
-                    self.model_runner.model,
-                    bs in self.compile_bs,
-                    num_tokens=bs * self.num_tokens_per_bs,
-                    tp_group=self.model_runner.tp_group,
-                ) as forward:
-                    (
-                        graph,
-                        output_buffers,
-                    ) = self.capture_one_batch_size(bs, forward)
-                    self.graphs[bs] = graph
-                    self.output_buffers[bs] = output_buffers
 
-                # Save gemlite cache after each capture
-                save_gemlite_cache()
+        with graph_capture() as graph_capture_context:
+            with profile_context as prof:
+                self.stream = graph_capture_context.stream
+                avail_mem = get_available_gpu_memory(
+                    self.model_runner.device,
+                    self.model_runner.gpu_id,
+                    empty_cache=False,
+                )
+                # Reverse the order to enable better memory sharing across cuda graphs.
+                capture_range = (
+                    tqdm.tqdm(list(reversed(self.capture_bs)))
+                    if get_tensor_model_parallel_rank() == 0
+                    else reversed(self.capture_bs)
+                )
+                for i, bs in enumerate(capture_range):
+                    if get_tensor_model_parallel_rank() == 0:
+                        avail_mem = get_available_gpu_memory(
+                            self.model_runner.device,
+                            self.model_runner.gpu_id,
+                            empty_cache=False,
+                        )
+                        capture_range.set_description(
+                            f"Capturing batches ({avail_mem=:.2f} GB)"
+                        )
+
+                    with patch_model(
+                        self.model_runner.model,
+                        bs in self.compile_bs,
+                        num_tokens=bs * self.num_tokens_per_bs,
+                        tp_group=self.model_runner.tp_group,
+                    ) as forward:
+                        (
+                            graph,
+                            output_buffers,
+                        ) = self.capture_one_batch_size(bs, forward)
+                        self.graphs[bs] = graph
+                        self.output_buffers[bs] = output_buffers
+
+                    # Save gemlite cache after each capture
+                    save_gemlite_cache()
+
+        if self.enable_profile_cuda_graph:
+            log_message = (
+                "Sorted by CUDA Time:\n"
+                + prof.key_averages(group_by_input_shape=True).table(
+                    sort_by="cuda_time_total", row_limit=10
+                )
+                + "\n\nSorted by CPU Time:\n"
+                + prof.key_averages(group_by_input_shape=True).table(
+                    sort_by="cpu_time_total", row_limit=10
+                )
+            )
+            logger.info(log_message)
 
     def capture_one_batch_size(self, bs: int, forward: Callable):
         graph = torch.cuda.CUDAGraph()
@@ -443,7 +474,7 @@ class CudaGraphRunner:
             token_to_kv_pool=self.model_runner.token_to_kv_pool,
             attn_backend=self.model_runner.attn_backend,
             out_cache_loc=out_cache_loc,
-            seq_lens_sum=seq_lens.sum(),
+            seq_lens_sum=seq_lens.sum().item(),
             encoder_lens=encoder_lens,
             return_logprob=False,
             positions=positions,

python/sglang/srt/server_args.py

@@ -112,14 +112,12 @@ class ServerArgs:
     file_storage_path: str = "sglang_storage"
     enable_cache_report: bool = False
     reasoning_parser: Optional[str] = None
+    tool_call_parser: Optional[str] = None
 
     # Data parallelism
     dp_size: int = 1
     load_balance_method: str = "round_robin"
 
-    # Expert parallelism
-    ep_size: int = 1
-
     # Multi-node distributed serving
     dist_init_addr: Optional[str] = None
     nnodes: int = 1
@@ -138,6 +136,7 @@ class ServerArgs:
     attention_backend: Optional[str] = None
     sampling_backend: Optional[str] = None
     grammar_backend: Optional[str] = None
+    mm_attention_backend: Optional[str] = None
 
     # Speculative decoding
     speculative_algorithm: Optional[str] = None
@@ -149,6 +148,26 @@ class ServerArgs:
     speculative_accept_threshold_acc: float = 1.0
     speculative_token_map: Optional[str] = None
 
+    # Expert parallelism
+    ep_size: int = 1
+    enable_ep_moe: bool = False
+    enable_deepep_moe: bool = False
+    deepep_mode: Optional[Literal["auto", "normal", "low_latency"]] = "auto"
+    ep_num_redundant_experts: int = 0
+    ep_dispatch_algorithm: Optional[Literal["static", "dynamic", "fake"]] = None
+    init_expert_location: str = "trivial"
+    enable_eplb: bool = False
+    eplb_algorithm: str = "auto"
+    eplb_rebalance_num_iterations: int = 1000
+    eplb_rebalance_layers_per_chunk: Optional[int] = None
+    expert_distribution_recorder_mode: Optional[
+        Literal["stat", "stat_approx", "per_pass", "per_token"]
+    ] = None
+    expert_distribution_recorder_buffer_size: Optional[int] = None
+    enable_expert_distribution_metrics: bool = False
+    deepep_config: Optional[str] = None
+    moe_dense_tp_size: Optional[int] = None
+
     # Double Sparsity
     enable_double_sparsity: bool = False
     ds_channel_config_path: Optional[str] = None
@@ -159,38 +178,24 @@ class ServerArgs:
 
     # Optimization/debug options
     disable_radix_cache: bool = False
+    cuda_graph_max_bs: Optional[int] = None
+    cuda_graph_bs: Optional[List[int]] = None
     disable_cuda_graph: bool = False
     disable_cuda_graph_padding: bool = False
+    enable_profile_cuda_graph: bool = False
     enable_nccl_nvls: bool = False
     enable_tokenizer_batch_encode: bool = False
     disable_outlines_disk_cache: bool = False
     disable_custom_all_reduce: bool = False
     enable_mscclpp: bool = False
     disable_overlap_schedule: bool = False
+    disable_overlap_cg_plan: bool = False
     enable_mixed_chunk: bool = False
     enable_dp_attention: bool = False
     enable_dp_lm_head: bool = False
     enable_two_batch_overlap: bool = False
-    enable_ep_moe: bool = False
-    enable_deepep_moe: bool = False
-    deepep_mode: Optional[Literal["auto", "normal", "low_latency"]] = "auto"
-    ep_num_redundant_experts: int = 0
-    ep_dispatch_algorithm: Optional[Literal["static", "dynamic", "fake"]] = None
-    init_expert_location: str = "trivial"
-    enable_eplb: bool = False
-    eplb_algorithm: str = "auto"
-    eplb_rebalance_num_iterations: int = 1000
-    eplb_rebalance_layers_per_chunk: Optional[int] = None
-    expert_distribution_recorder_mode: Optional[
-        Literal["stat", "stat_approx", "per_pass", "per_token"]
-    ] = None
-    expert_distribution_recorder_buffer_size: Optional[int] = None
-    enable_expert_distribution_metrics: bool = False
-    deepep_config: Optional[str] = None
     enable_torch_compile: bool = False
     torch_compile_max_bs: int = 32
-    cuda_graph_max_bs: Optional[int] = None
-    cuda_graph_bs: Optional[List[int]] = None
     torchao_config: str = ""
     enable_nan_detection: bool = False
     enable_p2p_check: bool = False
@@ -201,29 +206,28 @@ class ServerArgs:
     enable_memory_saver: bool = False
     allow_auto_truncate: bool = False
     enable_custom_logit_processor: bool = False
-    tool_call_parser: Optional[str] = None
     enable_hierarchical_cache: bool = False
     hicache_ratio: float = 2.0
     hicache_size: int = 0
     hicache_write_policy: str = "write_through_selective"
     flashinfer_mla_disable_ragged: bool = False
-    warmups: Optional[str] = None
-    moe_dense_tp_size: Optional[int] = None
     disable_shared_experts_fusion: bool = False
     disable_chunked_prefix_cache: bool = False
     disable_fast_image_processor: bool = False
-    mm_attention_backend: Optional[str] = None
+    warmups: Optional[str] = None
 
     # Debug tensor dumps
     debug_tensor_dump_output_folder: Optional[str] = None
     debug_tensor_dump_input_file: Optional[str] = None
     debug_tensor_dump_inject: bool = False
+    debug_tensor_dump_prefill_only: bool = False
 
     # For PD disaggregation: can be "null" (not disaggregated), "prefill" (prefill-only), or "decode" (decode-only)
     disaggregation_mode: str = "null"
-    disaggregation_bootstrap_port: int = 8998
     disaggregation_transfer_backend: str = "mooncake"
+    disaggregation_bootstrap_port: int = 8998
     disaggregation_ib_device: Optional[str] = None
+    num_reserved_decode_tokens: int = 512  # used for decode kv cache offload in PD
     pdlb_url: Optional[str] = None
 
     def __post_init__(self):
@@ -390,7 +394,7 @@ class ServerArgs:
         if self.enable_eplb and (self.expert_distribution_recorder_mode is None):
             self.expert_distribution_recorder_mode = "stat"
             logger.info(
-                f"EPLB is enabled. The expert_distribution_recorder_mode is automatically set."
+                "EPLB is enabled. The expert_distribution_recorder_mode is automatically set."
             )
 
         if (self.enable_eplb or (self.init_expert_location is not None)) and (
@@ -398,7 +402,7 @@ class ServerArgs:
         ):
             self.ep_dispatch_algorithm = "static"
             logger.info(
-                f"EPLB is enabled or init_expert_location is provided. ep_dispatch_algorithm is configured."
+                "EPLB is enabled or init_expert_location is provided. ep_dispatch_algorithm is configured."
             )
 
         if self.enable_expert_distribution_metrics and (
@@ -929,6 +933,13 @@ class ServerArgs:
             default=ServerArgs.reasoning_parser,
             help=f"Specify the parser for reasoning models, supported parsers are: {list(ReasoningParser.DetectorMap.keys())}.",
         )
+        parser.add_argument(
+            "--tool-call-parser",
+            type=str,
+            choices=["qwen25", "mistral", "llama3", "deepseekv3", "pythonic"],
+            default=ServerArgs.tool_call_parser,
+            help="Specify the parser for handling tool-call interactions. Options include: 'qwen25', 'mistral', 'llama3', 'deepseekv3', and 'pythonic'.",
+        )
 
         # Data parallelism
         parser.add_argument(
@@ -949,15 +960,6 @@ class ServerArgs:
             ],
         )
 
-        # Expert parallelism
-        parser.add_argument(
-            "--expert-parallel-size",
-            "--ep-size",
-            type=int,
-            default=ServerArgs.ep_size,
-            help="The expert parallelism size.",
-        )
-
         # Multi-node distributed serving
         parser.add_argument(
             "--dist-init-addr",
@@ -1038,21 +1040,6 @@ class ServerArgs:
             default=ServerArgs.grammar_backend,
             help="Choose the backend for grammar-guided decoding.",
         )
-        parser.add_argument(
-            "--enable-flashinfer-mla",
-            action=DeprecatedAction,
-            help="--enable-flashinfer-mla is deprecated. Please use '--attention-backend flashinfer' instead.",
-        )
-        parser.add_argument(
-            "--enable-flashmla",
-            action=DeprecatedAction,
-            help="--enable-flashmla is deprecated. Please use '--attention-backend flashmla' instead.",
-        )
-        parser.add_argument(
-            "--flashinfer-mla-disable-ragged",
-            action="store_true",
-            help="Not using ragged prefill wrapper when running flashinfer mla",
-        )
 
         # Speculative decoding
         parser.add_argument(
@@ -1102,6 +1089,109 @@ class ServerArgs:
             help="The path of the draft model's small vocab table.",
             default=ServerArgs.speculative_token_map,
         )
+        parser.add_argument(
+            "--mm-attention-backend",
+            type=str,
+            choices=["sdpa", "fa3", "triton_attn"],
+            default=ServerArgs.mm_attention_backend,
+            help="Set multimodal attention backend.",
+        )
+
+        # Expert parallelism
+        parser.add_argument(
+            "--expert-parallel-size",
+            "--ep-size",
+            type=int,
+            default=ServerArgs.ep_size,
+            help="The expert parallelism size.",
+        )
+        parser.add_argument(
+            "--enable-ep-moe",
+            action="store_true",
+            help="Enabling expert parallelism for moe. The ep size is equal to the tp size.",
+        )
+        parser.add_argument(
+            "--enable-deepep-moe",
+            action="store_true",
+            help="Enabling DeepEP MoE implementation for EP MoE.",
+        )
+        parser.add_argument(
+            "--deepep-mode",
+            type=str,
+            choices=["normal", "low_latency", "auto"],
+            default="auto",
+            help="Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.",
+        )
+        parser.add_argument(
+            "--ep-num-redundant-experts",
+            type=int,
+            default=ServerArgs.ep_num_redundant_experts,
+            help="Allocate this number of redundant experts in expert parallel.",
+        )
+        parser.add_argument(
+            "--ep-dispatch-algorithm",
+            type=str,
+            default=ServerArgs.ep_dispatch_algorithm,
+            help="The algorithm to choose ranks for redundant experts in expert parallel.",
+        )
+        parser.add_argument(
+            "--init-expert-location",
+            type=str,
+            default=ServerArgs.init_expert_location,
+            help="Initial location of EP experts.",
+        )
+        parser.add_argument(
+            "--enable-eplb",
+            action="store_true",
+            help="Enable EPLB algorithm",
+        )
+        parser.add_argument(
+            "--eplb-algorithm",
+            type=str,
+            default=ServerArgs.eplb_algorithm,
+            help="Chosen EPLB algorithm",
+        )
+        parser.add_argument(
+            "--eplb-rebalance-num-iterations",
+            type=int,
+            default=ServerArgs.eplb_rebalance_num_iterations,
+            help="Number of iterations to automatically trigger a EPLB re-balance.",
+        )
+        parser.add_argument(
+            "--eplb-rebalance-layers-per-chunk",
+            type=int,
+            default=ServerArgs.eplb_rebalance_layers_per_chunk,
+            help="Number of layers to rebalance per forward pass.",
+        )
+        parser.add_argument(
+            "--expert-distribution-recorder-mode",
+            type=str,
+            default=ServerArgs.expert_distribution_recorder_mode,
+            help="Mode of expert distribution recorder.",
+        )
+        parser.add_argument(
+            "--expert-distribution-recorder-buffer-size",
+            type=int,
+            default=ServerArgs.expert_distribution_recorder_buffer_size,
+            help="Circular buffer size of expert distribution recorder. Set to -1 to denote infinite buffer.",
+        )
+        parser.add_argument(
+            "--enable-expert-distribution-metrics",
+            action="store_true",
+            help="Enable logging metrics for expert balancedness",
+        )
+        parser.add_argument(
+            "--deepep-config",
+            type=str,
+            default=ServerArgs.deepep_config,
+            help="Tuned DeepEP config suitable for your own cluster. It can be either a string with JSON content or a file path.",
+        )
+        parser.add_argument(
+            "--moe-dense-tp-size",
+            type=int,
+            default=ServerArgs.moe_dense_tp_size,
+            help="TP size for MoE dense MLP layers. This flag is useful when, with large TP size, there are errors caused by weights in MLP layers having dimension smaller than the min dimension GEMM supports.",
+        )
 
         # Double Sparsity
         parser.add_argument(
@@ -1146,6 +1236,18 @@ class ServerArgs:
             action="store_true",
             help="Disable RadixAttention for prefix caching.",
         )
+        parser.add_argument(
+            "--cuda-graph-max-bs",
+            type=int,
+            default=ServerArgs.cuda_graph_max_bs,
+            help="Set the maximum batch size for cuda graph. It will extend the cuda graph capture batch size to this value.",
+        )
+        parser.add_argument(
+            "--cuda-graph-bs",
+            type=int,
+            nargs="+",
+            help="Set the list of batch sizes for cuda graph.",
+        )
         parser.add_argument(
             "--disable-cuda-graph",
             action="store_true",
@@ -1156,6 +1258,11 @@ class ServerArgs:
             action="store_true",
             help="Disable cuda graph when padding is needed. Still uses cuda graph when padding is not needed.",
         )
+        parser.add_argument(
+            "--enable-profile-cuda-graph",
+            action="store_true",
+            help="Enable profiling of cuda graph capture.",
+        )
         parser.add_argument(
             "--enable-nccl-nvls",
             action="store_true",
@@ -1186,6 +1293,11 @@ class ServerArgs:
             action="store_true",
             help="Disable the overlap scheduler, which overlaps the CPU scheduler with GPU model worker.",
         )
+        parser.add_argument(
+            "--disable-overlap-cg-plan",
+            action="store_true",
+            help="Disable the overlap optimization for cudagraph preparation in eagle verify.",
+        )
         parser.add_argument(
             "--enable-mixed-chunk",
             action="store_true",
@@ -1201,11 +1313,6 @@ class ServerArgs:
             action="store_true",
             help="Enable vocabulary parallel across the attention TP group to avoid all-gather across DP groups, optimizing performance under DP attention.",
         )
-        parser.add_argument(
-            "--enable-ep-moe",
-            action="store_true",
-            help="Enabling expert parallelism for moe. The ep size is equal to the tp size.",
-        )
         parser.add_argument(
             "--enable-two-batch-overlap",
             action="store_true",
@@ -1222,18 +1329,6 @@ class ServerArgs:
             default=ServerArgs.torch_compile_max_bs,
             help="Set the maximum batch size when using torch compile.",
         )
-        parser.add_argument(
-            "--cuda-graph-max-bs",
-            type=int,
-            default=ServerArgs.cuda_graph_max_bs,
-            help="Set the maximum batch size for cuda graph. It will extend the cuda graph capture batch size to this value.",
-        )
-        parser.add_argument(
-            "--cuda-graph-bs",
-            type=int,
-            nargs="+",
-            help="Set the list of batch sizes for cuda graph.",
-        )
         parser.add_argument(
             "--torchao-config",
             type=str,
@@ -1290,13 +1385,6 @@ class ServerArgs:
             action="store_true",
             help="Enable users to pass custom logit processors to the server (disabled by default for security)",
         )
-        parser.add_argument(
-            "--tool-call-parser",
-            type=str,
-            choices=["qwen25", "mistral", "llama3", "deepseekv3", "pythonic"],
-            default=ServerArgs.tool_call_parser,
-            help="Specify the parser for handling tool-call interactions. Options include: 'qwen25', 'mistral', 'llama3', 'deepseekv3', and 'pythonic'.",
-        )
         parser.add_argument(
             "--enable-hierarchical-cache",
             action="store_true",
@@ -1322,86 +1410,9 @@ class ServerArgs:
             help="The write policy of hierarchical cache.",
         )
         parser.add_argument(
-            "--enable-deepep-moe",
-            action="store_true",
-            help="Enabling DeepEP MoE implementation for EP MoE.",
-        )
-        parser.add_argument(
-            "--moe-dense-tp-size",
-            type=int,
-            default=ServerArgs.moe_dense_tp_size,
-            help="TP size for MoE dense MLP layers. This flag is useful when, with large TP size, there are errors caused by weights in MLP layers having dimension smaller than the min dimension GEMM supports.",
-        )
-        parser.add_argument(
-            "--deepep-mode",
-            type=str,
-            choices=["normal", "low_latency", "auto"],
-            default="auto",
-            help="Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.",
-        )
-        parser.add_argument(
-            "--ep-num-redundant-experts",
-            type=int,
-            default=ServerArgs.ep_num_redundant_experts,
-            help="Allocate this number of redundant experts in expert parallel.",
-        )
-        parser.add_argument(
-            "--ep-dispatch-algorithm",
-            type=str,
-            default=ServerArgs.ep_dispatch_algorithm,
-            help="The algorithm to choose ranks for redundant experts in expert parallel.",
-        )
-        parser.add_argument(
-            "--init-expert-location",
-            type=str,
-            default=ServerArgs.init_expert_location,
-            help="Initial location of EP experts.",
-        )
-        parser.add_argument(
-            "--enable-eplb",
-            action="store_true",
-            help="Enable EPLB algorithm",
-        )
-        parser.add_argument(
-            "--eplb-algorithm",
-            type=str,
-            default=ServerArgs.eplb_algorithm,
-            help="Chosen EPLB algorithm",
-        )
-        parser.add_argument(
-            "--eplb-rebalance-num-iterations",
-            type=int,
-            default=ServerArgs.eplb_rebalance_num_iterations,
-            help="Number of iterations to automatically trigger a EPLB re-balance.",
-        )
-        parser.add_argument(
-            "--eplb-rebalance-layers-per-chunk",
-            type=int,
-            default=ServerArgs.eplb_rebalance_layers_per_chunk,
-            help="Number of layers to rebalance per forward pass.",
-        )
-        parser.add_argument(
-            "--expert-distribution-recorder-mode",
-            type=str,
-            default=ServerArgs.expert_distribution_recorder_mode,
-            help="Mode of expert distribution recorder.",
-        )
-        parser.add_argument(
-            "--expert-distribution-recorder-buffer-size",
-            type=int,
-            default=ServerArgs.expert_distribution_recorder_buffer_size,
-            help="Circular buffer size of expert distribution recorder. Set to -1 to denote infinite buffer.",
-        )
-        parser.add_argument(
-            "--enable-expert-distribution-metrics",
+            "--flashinfer-mla-disable-ragged",
             action="store_true",
-            help="Enable logging metrics for expert balancedness",
-        )
-        parser.add_argument(
-            "--deepep-config",
-            type=str,
-            default=ServerArgs.deepep_config,
-            help="Tuned DeepEP config suitable for your own cluster. It can be either a string with JSON content or a file path.",
+            help="Not using ragged prefill wrapper when running flashinfer mla",
         )
         parser.add_argument(
             "--disable-shared-experts-fusion",
@@ -1418,8 +1429,6 @@ class ServerArgs:
             action="store_true",
             help="Adopt base image processor instead of fast image processor.",
         )
-
-        # Server warmups
         parser.add_argument(
             "--warmups",
             type=str,
@@ -1447,6 +1456,11 @@ class ServerArgs:
             default=ServerArgs.debug_tensor_dump_inject,
             help="Inject the outputs from jax as the input of every layer.",
         )
+        parser.add_argument(
+            "--debug-tensor-dump-prefill-only",
+            action="store_true",
+            help="Only dump the tensors for prefill requests (i.e. batch size > 1).",
+        )
 
         # Disaggregation
         parser.add_argument(
@@ -1456,12 +1470,6 @@ class ServerArgs:
             choices=["null", "prefill", "decode"],
             help='Only used for PD disaggregation. "prefill" for prefill-only server, and "decode" for decode-only server. If not specified, it is not PD disaggregated',
         )
-        parser.add_argument(
-            "--disaggregation-bootstrap-port",
-            type=int,
-            default=ServerArgs.disaggregation_bootstrap_port,
-            help="Bootstrap server port on the prefill server. Default is 8998.",
-        )
         parser.add_argument(
             "--disaggregation-transfer-backend",
             type=str,
@@ -1469,6 +1477,12 @@ class ServerArgs:
             choices=["mooncake", "nixl"],
             help="The backend for disaggregation transfer. Default is mooncake.",
         )
+        parser.add_argument(
+            "--disaggregation-bootstrap-port",
+            type=int,
+            default=ServerArgs.disaggregation_bootstrap_port,
+            help="Bootstrap server port on the prefill server. Default is 8998.",
+        )
         parser.add_argument(
             "--disaggregation-ib-device",
             type=str,
@@ -1477,6 +1491,12 @@ class ServerArgs:
             "or multiple comma-separated devices (e.g., --disaggregation-ib-device mlx5_0,mlx5_1). "
             "Default is None, which triggers automatic device detection when mooncake backend is enabled.",
         )
+        parser.add_argument(
+            "--num-reserved-decode-tokens",
+            type=int,
+            default=ServerArgs.num_reserved_decode_tokens,
+            help="Number of decode tokens that will have memory reserved when adding new request to the running batch.",
+        )
         parser.add_argument(
             "--pdlb-url",
             type=str,
@@ -1484,14 +1504,6 @@ class ServerArgs:
             help="The URL of the PD disaggregation load balancer. If set, the prefill/decode server will register with the load balancer.",
         )
 
-        parser.add_argument(
-            "--mm-attention-backend",
-            type=str,
-            choices=["sdpa", "fa3", "triton_attn"],
-            default=ServerArgs.mm_attention_backend,
-            help="Set multimodal attention backend.",
-        )
-
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):
         args.tp_size = args.tensor_parallel_size

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -41,6 +41,9 @@ class EAGLEDraftCudaGraphRunner:
         self.tp_size = self.model_runner.tp_size
         self.topk = model_runner.server_args.speculative_eagle_topk
         self.speculative_num_steps = model_runner.server_args.speculative_num_steps
+        self.enable_profile_cuda_graph = (
+            model_runner.server_args.enable_profile_cuda_graph
+        )
         server_args = model_runner.server_args
 
         # Batch sizes to capture

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -39,6 +39,9 @@ class EAGLEDraftExtendCudaGraphRunner:
         self.dp_size = model_runner.server_args.dp_size
         self.speculative_num_steps = model_runner.server_args.speculative_num_steps
         self.topk = model_runner.server_args.speculative_eagle_topk
+        self.enable_profile_cuda_graph = (
+            model_runner.server_args.enable_profile_cuda_graph
+        )
         self.capture_bs, self.compile_bs = get_batch_sizes_to_capture(model_runner)
         self.padded_static_len = -1
 

python/sglang/srt/utils.py

@@ -837,6 +837,7 @@ class CustomCacheManager(FileCacheManager):
 
 
 def set_ulimit(target_soft_limit=65535):
+    # number of open files
     resource_type = resource.RLIMIT_NOFILE
     current_soft, current_hard = resource.getrlimit(resource_type)
 
@@ -846,6 +847,18 @@ def set_ulimit(target_soft_limit=65535):
         except ValueError as e:
             logger.warning(f"Fail to set RLIMIT_NOFILE: {e}")
 
+    # stack size
+    resource_type = resource.RLIMIT_STACK
+    current_soft, current_hard = resource.getrlimit(resource_type)
+    target_soft_limit_stack_size = 1024 * target_soft_limit
+    if current_soft < target_soft_limit_stack_size:
+        try:
+            resource.setrlimit(
+                resource_type, (target_soft_limit_stack_size, current_hard)
+            )
+        except ValueError as e:
+            logger.warning(f"Fail to set RLIMIT_STACK: {e}")
+
 
 def add_api_key_middleware(app, api_key: str):
     @app.middleware("http")