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sglang/python/sglang/srt/server_args.py

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# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The arguments of the server."""
from __future__ import annotations
import argparse
import dataclasses
import importlib
import importlib.util
import json
import logging
import os
import random
import tempfile
from typing import Any, Callable, Dict, List, Literal, Optional, Union
from sglang.srt.configs.linear_attn_model_registry import get_linear_attn_spec_by_arch
from sglang.srt.connector import ConnectorType
from sglang.srt.environ import envs
from sglang.srt.function_call.function_call_parser import FunctionCallParser
from sglang.srt.layers.attention.fla.chunk_delta_h import CHUNK_SIZE as FLA_CHUNK_SIZE
from sglang.srt.lora.lora_registry import LoRARef
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.srt.utils.common import (
LORA_TARGET_ALL_MODULES,
SUPPORTED_LORA_TARGET_MODULES,
cpu_has_amx_support,
get_device,
get_device_memory_capacity,
get_device_name,
get_device_sm,
get_nvidia_driver_version,
get_quantization_config,
has_fp8_weights_in_checkpoint,
human_readable_int,
is_blackwell_supported,
is_cpu,
is_cuda,
is_flashinfer_available,
is_hip,
is_hopper_with_cuda_12_3,
is_mps,
is_musa,
is_no_spec_infer_or_topk_one,
is_npu,
is_remote_url,
is_sm90_supported,
is_sm100_supported,
is_sm120_supported,
is_triton_kernels_available,
is_xpu,
json_list_type,
nullable_str,
parse_connector_type,
torch_release,
xpu_has_xmx_support,
)
from sglang.srt.utils.hf_transformers_utils import check_gguf_file
from sglang.srt.utils.network import NetworkAddress, get_free_port, wait_port_available
from sglang.srt.utils.runai_utils import ObjectStorageModel, is_runai_obj_uri
from sglang.utils import is_in_ci
logger = logging.getLogger(__name__)
# Define constants
DEFAULT_UVICORN_ACCESS_LOG_EXCLUDE_PREFIXES = ()
SAMPLING_BACKEND_CHOICES = {"flashinfer", "pytorch", "ascend"}
LOAD_FORMAT_CHOICES = [
"auto",
"pt",
"safetensors",
"npcache",
"dummy",
"sharded_state",
"gguf",
"bitsandbytes",
"mistral",
"layered",
"flash_rl",
"remote",
"remote_instance",
"fastsafetensors",
"private",
"runai_streamer",
]
QUANTIZATION_CHOICES = [
"awq",
"fp8",
"mxfp8",
"gptq",
"marlin",
"gptq_marlin",
"awq_marlin",
"bitsandbytes",
"gguf",
"modelopt",
"modelopt_fp8",
"modelopt_fp4",
"modelopt_mixed",
"petit_nvfp4",
"w8a8_int8",
"w8a8_fp8",
"moe_wna16",
"qoq",
"w4afp8",
"mxfp4",
"auto-round",
"compressed-tensors", # for Ktransformers
"modelslim", # for NPU
"quark_int4fp8_moe",
"unquant",
]
SPECULATIVE_DRAFT_MODEL_QUANTIZATION_CHOICES = QUANTIZATION_CHOICES
ATTENTION_BACKEND_CHOICES = [
# Common
"triton",
"torch_native",
"flex_attention",
"nsa",
# NVIDIA specific
"cutlass_mla",
"fa3",
"fa4",
"flashinfer",
"flashmla",
"trtllm_mla",
"trtllm_mha",
"dual_chunk_flash_attn",
# AMD specific
"aiter",
"wave",
# Other platforms
"intel_amx",
"ascend",
"intel_xpu",
]
DETERMINISTIC_ATTENTION_BACKEND_CHOICES = ["flashinfer", "fa3", "triton"]
RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND = ["fa3", "triton"]
DISAGG_TRANSFER_BACKEND_CHOICES = ["mooncake", "nixl", "ascend", "fake", "mori"]
GRAMMAR_BACKEND_CHOICES = ["xgrammar", "outlines", "llguidance", "none"]
# Placeholder token inserted between items in Multi-Item Scoring sequences:
# query<delim>item1<delim>item2<delim>... Positions are pre-computed from item
# lengths (multi_item_delimiter_indices); the token only exists for FlashInfer
# attention mask compat and logprob column indexing. Will be removed once the
# attention backend supports position-only MIS.
MIS_DELIMITER_TOKEN_ID = 9999
MOE_RUNNER_BACKEND_CHOICES = [
"auto",
"deep_gemm",
"triton",
"triton_kernel",
"flashinfer_trtllm",
"flashinfer_trtllm_routed",
"flashinfer_cutlass",
"flashinfer_mxfp4",
"flashinfer_cutedsl",
"cutlass",
]
MOE_A2A_BACKEND_CHOICES = [
"none",
"deepep",
"mooncake",
"nixl",
"mori",
"ascend_fuseep",
"flashinfer",
]
FP8_GEMM_RUNNER_BACKEND_CHOICES = [
"auto",
"deep_gemm",
"flashinfer_trtllm",
"flashinfer_cutlass",
"flashinfer_deepgemm",
"cutlass",
"triton",
"aiter",
]
FP4_GEMM_RUNNER_BACKEND_CHOICES = [
"auto",
"cutlass",
"flashinfer_cudnn",
"flashinfer_cutlass",
"flashinfer_trtllm",
]
RADIX_EVICTION_POLICY_CHOICES = ["lru", "lfu", "slru", "priority"]
RL_ON_POLICY_TARGET_CHOICES = ["fsdp"]
LORA_BACKEND_CHOICES = ["triton", "csgmv", "ascend", "torch_native"]
ENCODER_TRANSFER_BACKEND_CHOICES = ["zmq_to_scheduler", "zmq_to_tokenizer", "mooncake"]
NSA_PREFILL_CP_SPLIT_CHOICES = ["in-seq-split", "round-robin-split"]
PREFILL_CP_SPLIT_CHOICES = ["in-seq-split"]
DEFAULT_LORA_EVICTION_POLICY = "lru"
NSA_CHOICES = [
"flashmla_sparse",
"flashmla_kv",
"flashmla_auto",
"fa3",
"tilelang",
"aiter",
"trtllm",
]
MAMBA_SCHEDULER_STRATEGY_CHOICES = ["auto", "no_buffer", "extra_buffer"]
MAMBA_BACKEND_CHOICES = ["triton", "flashinfer"]
LINEAR_ATTN_KERNEL_BACKEND_CHOICES = ["triton", "cutedsl", "flashinfer"]
# Allow external code to add more choices
def add_load_format_choices(choices):
LOAD_FORMAT_CHOICES.extend(choices)
def add_quantization_method_choices(choices):
QUANTIZATION_CHOICES.extend(choices)
def add_attention_backend_choices(choices):
ATTENTION_BACKEND_CHOICES.extend(choices)
def add_deterministic_attention_backend_choices(choices):
DETERMINISTIC_ATTENTION_BACKEND_CHOICES.extend(choices)
def add_radix_supported_deterministic_attention_backend_choices(choices):
RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND.extend(choices)
def add_disagg_transfer_backend_choices(choices):
DISAGG_TRANSFER_BACKEND_CHOICES.extend(choices)
def add_grammar_backend_choices(choices):
GRAMMAR_BACKEND_CHOICES.extend(choices)
def add_moe_runner_backend_choices(choices):
MOE_RUNNER_BACKEND_CHOICES.extend(choices)
def add_fp8_gemm_runner_backend_choices(choices):
FP8_GEMM_RUNNER_BACKEND_CHOICES.extend(choices)
def add_fp4_gemm_runner_backend_choices(choices):
FP4_GEMM_RUNNER_BACKEND_CHOICES.extend(choices)
def add_radix_eviction_policy_choices(choices):
RADIX_EVICTION_POLICY_CHOICES.extend(choices)
def add_rl_on_policy_target_choices(choices):
RL_ON_POLICY_TARGET_CHOICES.extend(choices)
@dataclasses.dataclass
class ServerArgs:
"""
The arguments of the server.
NOTE: When you add new arguments, please make sure the order
in this class definition the same as the order in the function
`ServerArgs.add_cli_args`.
Please follow the existing style to group the new arguments into related groups or create new groups.
"""
# Model and tokenizer
model_path: str
tokenizer_path: Optional[str] = None
tokenizer_mode: str = "auto"
tokenizer_backend: str = "huggingface"
tokenizer_worker_num: int = 1
skip_tokenizer_init: bool = False
load_format: str = "auto"
model_loader_extra_config: str = "{}"
trust_remote_code: bool = False
context_length: Optional[int] = None
is_embedding: bool = False
enable_multimodal: Optional[bool] = None
revision: Optional[str] = None
model_impl: str = "auto"
# HTTP server
host: str = "127.0.0.1"
port: int = 30000
fastapi_root_path: str = ""
grpc_mode: bool = False
skip_server_warmup: bool = False
warmups: Optional[str] = None
nccl_port: Optional[int] = None
checkpoint_engine_wait_weights_before_ready: bool = False
# SSL/TLS
ssl_keyfile: Optional[str] = None
ssl_certfile: Optional[str] = None
ssl_ca_certs: Optional[str] = None
ssl_keyfile_password: Optional[str] = None
enable_ssl_refresh: bool = False
enable_http2: bool = False
# Quantization and data type
dtype: str = "auto"
quantization: Optional[str] = None
quantization_param_path: Optional[str] = None
kv_cache_dtype: str = "auto"
enable_fp32_lm_head: bool = False
modelopt_quant: Optional[Union[str, Dict]] = None
modelopt_checkpoint_restore_path: Optional[str] = None
modelopt_checkpoint_save_path: Optional[str] = None
modelopt_export_path: Optional[str] = None
quantize_and_serve: bool = False
rl_quant_profile: Optional[str] = None # For flash_rl load format
# Memory and scheduling
mem_fraction_static: Optional[float] = None
max_running_requests: Optional[int] = None
max_queued_requests: Optional[int] = None
max_total_tokens: Optional[int] = None
chunked_prefill_size: Optional[int] = None
enable_dynamic_chunking: bool = False
max_prefill_tokens: int = 16384
prefill_max_requests: Optional[int] = None
schedule_policy: str = "fcfs"
enable_priority_scheduling: bool = False
disable_priority_preemption: bool = False
default_priority_value: Optional[int] = None
abort_on_priority_when_disabled: bool = False
schedule_low_priority_values_first: bool = False
priority_scheduling_preemption_threshold: int = 10
schedule_conservativeness: float = 1.0
page_size: Optional[int] = None
swa_full_tokens_ratio: float = 0.8
disable_hybrid_swa_memory: bool = False
radix_eviction_policy: str = "lru"
enable_prefill_delayer: bool = False
prefill_delayer_max_delay_passes: int = 30
prefill_delayer_token_usage_low_watermark: Optional[float] = None
prefill_delayer_forward_passes_buckets: Optional[List[float]] = None
prefill_delayer_wait_seconds_buckets: Optional[List[float]] = None
# Runtime options
device: Optional[str] = None
tp_size: int = 1
pp_size: int = 1
pp_max_micro_batch_size: Optional[int] = None
pp_async_batch_depth: int = 0
stream_interval: int = 1
batch_notify_size: int = 16
stream_response_default_include_usage: bool = False
incremental_streaming_output: bool = False
enable_streaming_session: bool = False
random_seed: Optional[int] = None
constrained_json_whitespace_pattern: Optional[str] = None
constrained_json_disable_any_whitespace: bool = False
watchdog_timeout: float = 300
soft_watchdog_timeout: Optional[float] = None
dist_timeout: Optional[int] = None # timeout for torch.distributed
download_dir: Optional[str] = None
model_checksum: Optional[str] = None
base_gpu_id: int = 0
gpu_id_step: int = 1
sleep_on_idle: bool = False
use_ray: bool = False
custom_sigquit_handler: Optional[Callable] = None
# Logging
log_level: str = "info"
log_level_http: Optional[str] = None
log_requests: bool = False
log_requests_level: int = 2
log_requests_format: str = "text"
log_requests_target: Optional[List[str]] = None
uvicorn_access_log_exclude_prefixes: List[str] = dataclasses.field(
default_factory=lambda: list(DEFAULT_UVICORN_ACCESS_LOG_EXCLUDE_PREFIXES)
)
crash_dump_folder: Optional[str] = None
show_time_cost: bool = False
enable_metrics: bool = False
grpc_http_sidecar_port: Optional[int] = None
enable_mfu_metrics: bool = False
enable_metrics_for_all_schedulers: bool = False
tokenizer_metrics_custom_labels_header: str = "x-custom-labels"
tokenizer_metrics_allowed_custom_labels: Optional[List[str]] = None
extra_metric_labels: Optional[Dict[str, str]] = None
bucket_time_to_first_token: Optional[List[float]] = None
bucket_inter_token_latency: Optional[List[float]] = None
bucket_e2e_request_latency: Optional[List[float]] = None
prompt_tokens_buckets: Optional[List[str]] = None
generation_tokens_buckets: Optional[List[str]] = None
gc_warning_threshold_secs: float = 0.0
decode_log_interval: int = 40
enable_request_time_stats_logging: bool = False
kv_events_config: Optional[str] = None
enable_trace: bool = False
otlp_traces_endpoint: str = "localhost:4317"
# RequestMetricsExporter configuration
export_metrics_to_file: bool = False
export_metrics_to_file_dir: Optional[str] = None
# API related
api_key: Optional[str] = None
admin_api_key: Optional[str] = None
served_model_name: Optional[str] = None
weight_version: str = "default"
chat_template: Optional[str] = None
hf_chat_template_name: Optional[str] = None
completion_template: Optional[str] = None
file_storage_path: str = "sglang_storage"
enable_cache_report: bool = False
reasoning_parser: Optional[str] = None
strip_thinking_cache: bool = False
tool_call_parser: Optional[str] = None
tool_server: Optional[str] = None
sampling_defaults: str = "model"
# Data parallelism
dp_size: int = 1
load_balance_method: str = "auto"
attn_cp_size: int = 1
moe_dp_size: int = 1
# Multi-node distributed serving
dist_init_addr: Optional[str] = None
nnodes: int = 1
node_rank: int = 0
# Model override args in JSON
json_model_override_args: str = "{}"
preferred_sampling_params: Optional[str] = None
# LoRA
enable_lora: Optional[bool] = None
enable_lora_overlap_loading: Optional[bool] = None
max_lora_rank: Optional[int] = None
lora_target_modules: Optional[Union[set[str], List[str]]] = None
lora_paths: Optional[
Union[dict[str, str], List[dict[str, str]], List[str], List[LoRARef]]
] = None
max_loaded_loras: Optional[int] = None
max_loras_per_batch: int = 8
lora_eviction_policy: str = "lru"
lora_backend: str = "csgmv"
max_lora_chunk_size: Optional[int] = 16
experts_shared_outer_loras: Optional[bool] = None
lora_use_virtual_experts: bool = False
lora_strict_loading: bool = False
# Kernel backend
attention_backend: Optional[str] = None
decode_attention_backend: Optional[str] = None
prefill_attention_backend: Optional[str] = None
sampling_backend: Optional[str] = None
grammar_backend: Optional[str] = None
mm_attention_backend: Optional[str] = None
fp8_gemm_runner_backend: str = "auto"
fp4_gemm_runner_backend: str = "auto"
nsa_prefill_backend: Optional[str] = (
None # None = auto-detect based on hardware/kv_cache_dtype
)
nsa_decode_backend: Optional[str] = (
None # auto-detect based on hardware/kv_cache_dtype
)
disable_flashinfer_autotune: bool = False
mamba_backend: str = "triton"
# Speculative decoding
speculative_algorithm: Optional[str] = None
speculative_draft_model_path: Optional[str] = None
speculative_draft_model_revision: Optional[str] = None
speculative_draft_load_format: Optional[str] = None
speculative_num_steps: Optional[int] = None
speculative_eagle_topk: Optional[int] = None
speculative_num_draft_tokens: Optional[int] = None
speculative_dflash_block_size: Optional[int] = None
speculative_dflash_draft_window_size: Optional[int] = None
speculative_accept_threshold_single: float = 1.0
speculative_accept_threshold_acc: float = 1.0
speculative_token_map: Optional[str] = None
speculative_attention_mode: str = "prefill"
speculative_draft_attention_backend: Optional[str] = None
speculative_moe_runner_backend: Optional[str] = None
speculative_moe_a2a_backend: Optional[str] = None
speculative_draft_model_quantization: Optional[str] = None
speculative_adaptive: bool = False
speculative_adaptive_config: Optional[str] = None
# Speculative decoding (ngram)
speculative_ngram_min_bfs_breadth: int = 1
speculative_ngram_max_bfs_breadth: int = 10
speculative_ngram_match_type: Literal["BFS", "PROB"] = "BFS"
speculative_ngram_max_trie_depth: int = 18
speculative_ngram_capacity: int = 10 * 1000 * 1000
speculative_ngram_external_corpus_path: Optional[str] = None
speculative_ngram_external_sam_budget: int = 0
speculative_ngram_external_corpus_max_tokens: int = 10000000
enable_multi_layer_eagle: bool = False
# Expert parallelism
ep_size: int = 1
moe_a2a_backend: Literal[
"none", "deepep", "mooncake", "nixl", "mori", "ascend_fuseep", "flashinfer"
] = "none"
moe_runner_backend: str = "auto"
record_nolora_graph: bool = True
flashinfer_mxfp4_moe_precision: Literal["default", "bf16"] = "default"
enable_flashinfer_allreduce_fusion: bool = False
enforce_disable_flashinfer_allreduce_fusion: bool = False
enable_aiter_allreduce_fusion: bool = False
deepep_mode: 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
eplb_min_rebalancing_utilization_threshold: float = 1.0
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
elastic_ep_backend: Literal[None, "mooncake", "nixl"] = None
enable_elastic_expert_backup: bool = False
mooncake_ib_device: Optional[str] = None
elastic_ep_rejoin: bool = False
# Mamba cache
max_mamba_cache_size: Optional[int] = None
mamba_ssm_dtype: Optional[str] = None
mamba_full_memory_ratio: float = 0.9
mamba_scheduler_strategy: str = "auto"
mamba_track_interval: int = 256
linear_attn_backend: str = "triton"
linear_attn_decode_backend: Optional[str] = None
linear_attn_prefill_backend: Optional[str] = None
# Hierarchical cache
enable_hierarchical_cache: bool = False
hicache_ratio: float = 2.0
hicache_size: int = 0
hicache_write_policy: str = "write_through"
hicache_io_backend: str = "kernel"
hicache_mem_layout: str = "layer_first"
hicache_storage_backend: Optional[str] = None
hicache_storage_prefetch_policy: str = "best_effort"
hicache_storage_backend_extra_config: Optional[str] = None
# Hierarchical sparse attention
enable_hisparse: bool = False
hisparse_config: Optional[str] = None
# LMCache
enable_lmcache: bool = False
# Ktransformers/AMX expert parallelism
kt_weight_path: Optional[str] = None
kt_method: Optional[str] = None
kt_cpuinfer: Optional[int] = None
kt_threadpool_count: Optional[int] = None
kt_num_gpu_experts: Optional[int] = None
kt_max_deferred_experts_per_token: Optional[int] = None
# Diffusion LLM
dllm_algorithm: Optional[str] = None
dllm_algorithm_config: Optional[str] = None
# Offloading
cpu_offload_gb: int = 0
offload_group_size: int = -1
offload_num_in_group: int = 1
offload_prefetch_step: int = 1
offload_mode: str = "cpu"
# Scoring configuration
# Enable Multi-Item Scoring optimization. Combines query and multiple items
# into a single sequence for efficient batch processing. Item boundaries are
# determined by pre-computed delimiter indices (from item lengths), not by the
# placeholder token. See MIS_DELIMITER_TOKEN_ID for details.
enable_mis: bool = False
# 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_breakable_cuda_graph: bool = False
enable_profile_cuda_graph: bool = False
enable_cudagraph_gc: bool = False
debug_cuda_graph: bool = False
enable_layerwise_nvtx_marker: bool = False
enable_nccl_nvls: bool = False
enable_symm_mem: bool = False
disable_flashinfer_cutlass_moe_fp4_allgather: bool = False
enable_tokenizer_batch_encode: bool = False
disable_tokenizer_batch_decode: bool = False
disable_outlines_disk_cache: bool = False
disable_custom_all_reduce: bool = False
enable_mscclpp: bool = False
enable_torch_symm_mem: bool = False
pre_warm_nccl: bool = dataclasses.field(
default_factory=lambda: is_hip()
) # Pre-warm NCCL/RCCL to reduce P99 TTFT cold-start latency (default: True for AMD/HIP, False for others)
disable_overlap_schedule: bool = False
enable_mixed_chunk: bool = False
enable_dp_attention: bool = False
enable_dp_attention_local_control_broadcast: bool = False
enable_dp_lm_head: bool = False
enable_two_batch_overlap: bool = False
enable_single_batch_overlap: bool = False
tbo_token_distribution_threshold: float = 0.48
enable_torch_compile: bool = False
disable_piecewise_cuda_graph: bool = False
enforce_piecewise_cuda_graph: bool = False
enable_torch_compile_debug_mode: bool = False
torch_compile_max_bs: int = 32
piecewise_cuda_graph_max_tokens: Optional[int] = None
piecewise_cuda_graph_tokens: Optional[List[int]] = None
piecewise_cuda_graph_compiler: str = "eager"
torchao_config: str = ""
enable_nan_detection: bool = False
enable_p2p_check: bool = False
triton_attention_reduce_in_fp32: bool = False
triton_attention_num_kv_splits: int = 8
triton_attention_split_tile_size: Optional[int] = None
num_continuous_decode_steps: int = 1
delete_ckpt_after_loading: bool = False
enable_memory_saver: bool = False
enable_weights_cpu_backup: bool = False
enable_draft_weights_cpu_backup: bool = False
allow_auto_truncate: bool = False
enable_custom_logit_processor: bool = False
flashinfer_mla_disable_ragged: bool = False
disable_shared_experts_fusion: bool = False
enforce_shared_experts_fusion: bool = False
disable_chunked_prefix_cache: bool = False
disable_fast_image_processor: bool = False
keep_mm_feature_on_device: bool = False
enable_return_hidden_states: bool = False
enable_return_routed_experts: bool = False
scheduler_recv_interval: int = 1
numa_node: Optional[List[int]] = None
enable_deterministic_inference: bool = False
rl_on_policy_target: Optional[str] = None
enable_attn_tp_input_scattered: bool = False
gc_threshold: Optional[List[int]] = None
# Context parallelism used in the long sequence prefill phase of DeepSeek v3.2
enable_nsa_prefill_context_parallel: bool = False
nsa_prefill_cp_mode: str = "round-robin-split"
enable_fused_qk_norm_rope: bool = False
enable_precise_embedding_interpolation: bool = False
enable_fused_moe_sum_all_reduce: bool = False
# Context parallelism
enable_prefill_context_parallel: bool = False
prefill_cp_mode: str = "in-seq-split"
# Dynamic batch tokenizer
enable_dynamic_batch_tokenizer: bool = False
dynamic_batch_tokenizer_batch_size: int = 32
dynamic_batch_tokenizer_batch_timeout: float = 0.002
# Debug tensor dumps
debug_tensor_dump_output_folder: Optional[str] = None
# None means dump all layers.
debug_tensor_dump_layers: Optional[List[int]] = None
# TODO(guoyuhong): clean the old dumper code.
debug_tensor_dump_input_file: Optional[str] = None
debug_tensor_dump_inject: bool = False
# PD disaggregation: can be "null" (not disaggregated), "prefill" (prefill-only), or "decode" (decode-only)
disaggregation_mode: Literal["null", "prefill", "decode"] = "null"
disaggregation_transfer_backend: str = "mooncake"
disaggregation_bootstrap_port: int = 8998
disaggregation_ib_device: Optional[str] = None
disaggregation_decode_enable_offload_kvcache: bool = False
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
# Encode prefill disaggregation
encoder_only: bool = False
language_only: bool = False
encoder_transfer_backend: str = ENCODER_TRANSFER_BACKEND_CHOICES[0]
encoder_urls: List[str] = dataclasses.field(default_factory=list)
enable_adaptive_dispatch_to_encoder: bool = False
# For model weight update and weight loading
custom_weight_loader: Optional[List[str]] = None
weight_loader_disable_mmap: bool = False
weight_loader_prefetch_checkpoints: bool = False
weight_loader_prefetch_num_threads: int = 4
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
remote_instance_weight_loader_backend: Literal[
"transfer_engine", "nccl", "modelexpress"
] = "nccl"
remote_instance_weight_loader_start_seed_via_transfer_engine: bool = False
engine_info_bootstrap_port: int = 6789
modelexpress_config: Optional[str] = None
# For PD-Multiplexing
enable_pdmux: bool = False
pdmux_config_path: Optional[str] = None
sm_group_num: int = 8
# For Multi-Modal
enable_broadcast_mm_inputs_process: bool = False
enable_prefix_mm_cache: bool = False
mm_enable_dp_encoder: bool = False
mm_process_config: Optional[Dict[str, Any]] = None
limit_mm_data_per_request: Optional[Union[str, Dict[str, int]]] = None
enable_mm_global_cache: bool = False
# For checkpoint decryption
decrypted_config_file: Optional[str] = None
decrypted_draft_config_file: Optional[str] = None
# For forward hooks
forward_hooks: Optional[List[dict[str, Any]]] = None
# For msProbe
msprobe_dump_config: Optional[str] = None
def __post_init__(self):
"""
Orchestrates the handling of various server arguments, ensuring proper configuration and validation.
"""
self._maybe_download_model_for_runai()
# Normalize load balancing defaults early (before dummy-model short-circuit).
self._handle_load_balance_method()
# Validate mm_process_config before dummy-model early return.
self._handle_multimodal()
# Validate SSL arguments early (before dummy-model short-circuit).
self._handle_ssl_validation()
if self.model_path.lower() in ["none", "dummy"]:
# Skip for dummy models
return
# Handle deprecated arguments.
self._handle_deprecated_args()
# Handle deprecated environment variables for prefill delayer.
self._handle_prefill_delayer_env_compat()
# Resolve --quantization unquant: explicitly opt out of quantization.
# Convert to None now (before model config validation), but record
# the intent so auto-detection in _handle_model_specific_adjustments
# does not override it.
if self.quantization == "unquant":
self.quantization = None
self._quantization_explicitly_unset = True
else:
self._quantization_explicitly_unset = False
# Set missing default values.
self._handle_missing_default_values()
# Handle device-specific backends.
self._handle_hpu_backends()
self._handle_cpu_backends()
self._handle_npu_backends()
self._handle_mps_backends()
self._handle_xpu_backends()
# Allow OOT platform plugins to apply server args defaults.
from sglang.srt.platforms import current_platform
current_platform.apply_server_args_defaults(self)
# Handle piecewise CUDA graph.
self._handle_piecewise_cuda_graph()
# Get GPU memory capacity, which is a common dependency for several configuration steps.
gpu_mem = get_device_memory_capacity(self.device)
# Handle memory-related, chunked prefill, and CUDA graph batch size configurations.
self._handle_gpu_memory_settings(gpu_mem)
# Apply model-specific adjustments.
self._handle_model_specific_adjustments()
# Set kernel backends.
self._handle_sampling_backend()
self._handle_attention_backend_compatibility()
self._handle_mamba_backend()
self._handle_linear_attn_backend()
self._handle_kv4_compatibility()
self._handle_page_size()
self._handle_amd_specifics()
self._handle_nccl_pre_warm()
self._handle_grammar_backend()
# Handle multi-item scoring constraints. Must run after the above so
# the final attention backend and chunked_prefill_size are in effect.
self._handle_multi_item_scoring()
# Handle Hicache settings.
self._handle_hicache()
# Handle data parallelism.
self._handle_data_parallelism()
# Handle context parallelism.
self._handle_context_parallelism()
# Handle MoE configurations.
self._handle_moe_kernel_config()
self._handle_a2a_moe()
self._handle_eplb_and_dispatch()
self._handle_expert_distribution_metrics()
self._handle_elastic_ep()
# Handle pipeline parallelism.
self._handle_pipeline_parallelism()
# Handle speculative decoding logic.
self._handle_speculative_decoding()
# Handle model loading format.
self._handle_load_format()
# Handle PD disaggregation.
self._handle_pd_disaggregation()
# Handle Encoder disaggregation.
self._handle_encoder_disaggregation()
# Validate tokenizer settings.
self._handle_tokenizer_batching()
# Propagate environment variables.
self._handle_environment_variables()
# Validate cache settings.
self._handle_cache_compatibility()
# Handle deterministic inference.
self._handle_deterministic_inference()
# Handle diffusion LLM inference.
self._handle_dllm_inference()
# Handle debug utilities.
self._handle_debug_utils()
# Handle any other necessary validations.
self._handle_other_validations()
def _maybe_download_model_for_runai(self):
if is_runai_obj_uri(self.model_path):
ObjectStorageModel.download_and_get_path(self.model_path)
if (
self.tokenizer_path is not None
and is_runai_obj_uri(self.tokenizer_path)
and self.tokenizer_path != self.model_path
):
ObjectStorageModel.download_and_get_path(self.tokenizer_path)
def _handle_load_balance_method(self):
if self.disaggregation_mode not in ("null", "prefill", "decode"):
raise ValueError(
f"Invalid disaggregation_mode={self.disaggregation_mode!r}"
)
if self.load_balance_method == "auto":
# Default behavior:
# - non-PD: round_robin
# - PD prefill: follow_bootstrap_room
# - PD decode: round_robin
self.load_balance_method = (
"follow_bootstrap_room"
if self.disaggregation_mode == "prefill"
else "round_robin"
)
return
def _handle_ssl_validation(self):
"""Ensure SSL arguments are consistent and referenced files exist."""
if self.ssl_keyfile and not self.ssl_certfile:
raise ValueError(
"--ssl-keyfile requires --ssl-certfile to be specified as well."
)
if self.ssl_certfile and not self.ssl_keyfile:
raise ValueError(
"--ssl-certfile requires --ssl-keyfile to be specified as well."
)
if not self.ssl_certfile and not self.ssl_keyfile:
if self.ssl_ca_certs:
raise ValueError(
"--ssl-ca-certs has no effect without --ssl-certfile and --ssl-keyfile."
)
if self.ssl_keyfile_password:
raise ValueError(
"--ssl-keyfile-password has no effect without --ssl-certfile and --ssl-keyfile."
)
# Validate files exist early to avoid late failures after model loading.
if self.ssl_keyfile and not os.path.isfile(self.ssl_keyfile):
raise ValueError(
f"SSL key file not found: '{self.ssl_keyfile}'. "
f"Please check the --ssl-keyfile path."
)
if self.ssl_certfile and not os.path.isfile(self.ssl_certfile):
raise ValueError(
f"SSL certificate file not found: '{self.ssl_certfile}'. "
f"Please check the --ssl-certfile path."
)
if self.ssl_ca_certs and not os.path.isfile(self.ssl_ca_certs):
raise ValueError(
f"SSL CA certificates file not found: '{self.ssl_ca_certs}'. "
f"Please check the --ssl-ca-certs path."
)
if self.enable_ssl_refresh and not (self.ssl_certfile and self.ssl_keyfile):
raise ValueError(
"--enable-ssl-refresh requires --ssl-certfile and --ssl-keyfile "
"to be specified."
)
if self.enable_http2:
try:
import granian # noqa: F401
except ImportError:
raise ValueError(
"--enable-http2 requires the 'granian' package. "
'Install it with: pip install "sglang[http2]"'
)
if self.enable_ssl_refresh:
raise ValueError(
"--enable-ssl-refresh is not supported with --enable-http2. "
"Granian does not support SSL certificate hot-reloading. "
"Use Uvicorn (the default) or handle certificate rotation externally."
)
if self.tokenizer_worker_num > 1:
raise ValueError(
"--enable-http2 does not yet support --tokenizer-worker-num > 1. "
"Multi-worker HTTP/2 support will be added in a future release."
)
def _handle_multimodal(self):
"""Validate mm_process_config structure before model loading."""
if self.mm_process_config is not None:
if not isinstance(self.mm_process_config, dict):
raise TypeError(
f"mm_process_config must be a dict, "
f"but got {type(self.mm_process_config)}"
)
for key in ("image", "video", "audio"):
if key in self.mm_process_config and not isinstance(
self.mm_process_config[key], dict
):
raise TypeError(
f"mm_process_config['{key}'] must be a dict, "
f"but got {type(self.mm_process_config[key])}"
)
def _handle_deprecated_args(self):
# Handle deprecated tool call parsers
deprecated_tool_call_parsers = {"qwen25": "qwen", "glm45": "glm"}
if self.tool_call_parser in deprecated_tool_call_parsers:
logger.warning(
f"The tool_call_parser '{self.tool_call_parser}' is deprecated. Please use '{deprecated_tool_call_parsers[self.tool_call_parser]}' instead."
)
self.tool_call_parser = deprecated_tool_call_parsers[self.tool_call_parser]
if self.enable_nan_detection:
logger.warning(
"--enable-nan-detection is deprecated. "
"Use SGLANG_SPEC_NAN_DETECTION=1 and SGLANG_SPEC_OOB_DETECTION=1 instead."
)
envs.SGLANG_SPEC_NAN_DETECTION.set(True)
envs.SGLANG_SPEC_OOB_DETECTION.set(True)
# Native gRPC flags — env-only for now, not exposed as CLI args.
# Set as instance attributes (not dataclass fields) to avoid
# argparse namespace lookup in from_cli_args.
self.enable_grpc = envs.SGLANG_ENABLE_GRPC.get()
grpc_port_env = envs.SGLANG_GRPC_PORT.get()
self.grpc_port = (
grpc_port_env if grpc_port_env is not None else self.port + 10000
)
if not (1 <= self.grpc_port <= 65535):
raise ValueError(
f"SGLANG_GRPC_PORT ({self.grpc_port}) must be between 1 and 65535"
)
def _handle_prefill_delayer_env_compat(self):
if envs.SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE.get():
self.enable_prefill_delayer = True
if x := envs.SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES.get():
self.prefill_delayer_max_delay_passes = x
if x := envs.SGLANG_PREFILL_DELAYER_TOKEN_USAGE_LOW_WATERMARK.get():
self.prefill_delayer_token_usage_low_watermark = x
def _handle_missing_default_values(self):
if self.tokenizer_path is None:
self.tokenizer_path = self.model_path
if self.served_model_name is None:
self.served_model_name = self.model_path
if self.device is None:
self.device = get_device()
# strip device index from user if any (e.g. "cuda:0" -> "cuda")
self.device = self.device.split(":")[0]
if self.random_seed is None:
self.random_seed = random.randint(0, 1 << 30)
if self.mm_process_config is None:
self.mm_process_config = {}
# Handle ModelScope model downloads
if envs.SGLANG_USE_MODELSCOPE.get():
self._handle_modelscope_paths()
# Mamba scheduler strategy
if self.mamba_scheduler_strategy == "auto":
# TODO: when extra_buffer is more verified, we can set the default path based on
# [overlap, non-overlap]
self.mamba_scheduler_strategy = "no_buffer"
# In speculative scenario:
# - If `speculative_draft_model_quantization` is specified, the draft model uses this quantization method.
# - Otherwise, the draft model defaults to the same quantization as the target model.
if self.speculative_draft_model_quantization is None:
self.speculative_draft_model_quantization = self.quantization
elif self.speculative_draft_model_quantization == "unquant":
self.speculative_draft_model_quantization = None
def _handle_modelscope_paths(self):
"""Resolve model / tokenizer / speculative-draft paths from the local
ModelScope cache when possible, falling back to ``snapshot_download``
for any path that is not already present on disk.
Note: ``speculative_token_map`` is intentionally NOT handled here
because its value uses ``repo_id/filename`` semantics rather than a
plain repo ID. That resolution lives in
:func:`sglang.srt.speculative.spec_utils.load_token_map`.
"""
ms_root = None
ms_snapshot_download = None
def _resolve_or_download(
path: Optional[str],
ignore_patterns: Optional[list] = None,
revision: Optional[str] = None,
) -> Optional[str]:
nonlocal ms_root, ms_snapshot_download
if path is None:
return None
if not path or os.path.exists(path):
return path
if ms_snapshot_download is None:
from modelscope.hub.snapshot_download import (
snapshot_download as _ms_snapshot_download,
)
from modelscope.utils.file_utils import get_model_cache_root
ms_snapshot_download = _ms_snapshot_download
ms_root = get_model_cache_root()
# Check ModelScope default cache
cached = os.path.join(ms_root, path)
if os.path.exists(cached):
return cached
# Check user-specified download dir
if self.download_dir:
alt = os.path.join(self.download_dir, path)
if os.path.exists(alt):
return alt
# Cache miss — download from ModelScope hub
return ms_snapshot_download(
path,
cache_dir=self.download_dir,
revision=revision,
**({"ignore_patterns": ignore_patterns} if ignore_patterns else {}),
)
self.model_path = _resolve_or_download(self.model_path, revision=self.revision)
self.tokenizer_path = _resolve_or_download(
self.tokenizer_path,
ignore_patterns=["*.bin", "*.safetensors"],
revision=self.revision,
)
if self.speculative_draft_model_path:
self.speculative_draft_model_path = _resolve_or_download(
self.speculative_draft_model_path,
revision=self.speculative_draft_model_revision or "main",
)
def _handle_hpu_backends(self):
if self.device == "hpu":
self.attention_backend = "torch_native"
self.sampling_backend = "pytorch"
def _handle_cpu_backends(self):
if self.device == "cpu":
if self.attention_backend is None:
self.attention_backend = "intel_amx"
self.sampling_backend = "pytorch"
def _handle_npu_backends(self):
if self.device == "npu":
from sglang.srt.hardware_backend.npu.utils import set_default_server_args
set_default_server_args(self)
if self.piecewise_cuda_graph_compiler != "eager":
logger.warning(
"At this moment Ascend platform only support prefill graph compilation with "
"piecewise_cuda_graph_compiler='eager', change piecewise_cuda_graph_compiler to 'eager'."
)
self.piecewise_cuda_graph_compiler = "eager"
def _handle_mps_backends(self):
if self.device == "mps":
self.disable_overlap_schedule = True
def _handle_xpu_backends(self):
if self.device == "xpu":
if not self.disable_piecewise_cuda_graph:
logger.warning(
"XPU platform does not support piecewise CUDA graph, ignoring --disable-piecewise-cuda-graph"
" flag and disabling piecewise CUDA graph."
)
self.disable_piecewise_cuda_graph = True
def _handle_piecewise_cuda_graph(self):
# Skip auto-disable when enforce flag is set (for testing)
if self.enforce_piecewise_cuda_graph:
self.disable_piecewise_cuda_graph = False
return
# Disable piecewise cuda graph with following conditions:
# 1. Disable Model Arch
if self.get_model_config().is_piecewise_cuda_graph_disabled_model:
self.disable_piecewise_cuda_graph = True
# 2. DP attention
if self.enable_dp_attention:
self.disable_piecewise_cuda_graph = True
# 3. Torch compile
if self.enable_torch_compile:
self.disable_piecewise_cuda_graph = True
# 4. Pipeline parallelism
if self.pp_size > 1:
self.disable_piecewise_cuda_graph = True
# 5. Non-CUDA hardware (AMD, NPU, CPU, MPS, XPU, etc.)
if is_hip() or is_npu() or is_cpu() or is_mps() or is_xpu():
self.disable_piecewise_cuda_graph = True
# 5b. OOT platforms that don't support piecewise cuda graph
from sglang.srt.platforms import current_platform
if current_platform.is_out_of_tree():
if not current_platform.support_piecewise_cuda_graph():
self.disable_piecewise_cuda_graph = True
# 6. MoE A2A backend
if self.moe_a2a_backend != "none":
self.disable_piecewise_cuda_graph = True
# 7. LoRA
if self.lora_paths or self.enable_lora:
self.disable_piecewise_cuda_graph = True
# 8. Multimodal / VLM models
if self.get_model_config().is_multimodal:
self.disable_piecewise_cuda_graph = True
# 9. GGUF quantized models (custom dequant ops unsupported by torch.compile)
if (
self.load_format == "gguf"
or self.quantization == "gguf"
or check_gguf_file(self.model_path)
):
self.disable_piecewise_cuda_graph = True
# 10. DLLM (diffusion LLM) models (context manager in forward breaks dynamo)
if self.dllm_algorithm is not None:
self.disable_piecewise_cuda_graph = True
# 11. CPU offload (breaks dynamo)
if self.cpu_offload_gb > 0 or self.enable_hierarchical_cache:
self.disable_piecewise_cuda_graph = True
# 12. Deterministic inference
if self.enable_deterministic_inference:
self.disable_piecewise_cuda_graph = True
# 13. PD disaggregation
if self.disaggregation_mode != "null":
self.disable_piecewise_cuda_graph = True
# 14. Symmetric memory (torch.cuda.use_mem_pool is untraceable by dynamo)
if self.enable_symm_mem:
self.disable_piecewise_cuda_graph = True
# 15. Expert distribution recorder
if self.enable_eplb or self.expert_distribution_recorder_mode is not None:
self.disable_piecewise_cuda_graph = True
# 16. Context parallel
if self.attn_cp_size > 1:
self.disable_piecewise_cuda_graph = True
# 18. CUDA Graph debug mode
if self.debug_cuda_graph:
self.disable_piecewise_cuda_graph = True
def _handle_multi_item_scoring(self):
"""Setup and validate multi-item scoring constraints.
Auto-disables settings incompatible with MIS mechanics (CUDA graph,
radix cache, chunked prefill). Asserts on attention backend since
changing it silently could surprise users who intentionally picked
a non-flashinfer backend.
"""
if not self.enable_mis:
return
if not self.disable_cuda_graph:
logger.warning("CUDA graph is disabled because --enable-mis is set.")
self.disable_cuda_graph = True
self.disable_piecewise_cuda_graph = True
if not self.disable_radix_cache:
logger.warning("Radix cache is disabled because --enable-mis is set.")
self.disable_radix_cache = True
if self.chunked_prefill_size != -1:
logger.warning("Chunked prefill is disabled because --enable-mis is set.")
self.chunked_prefill_size = -1
prefill_backend, decode_backend = self.get_attention_backends()
assert prefill_backend == "flashinfer" and decode_backend == "flashinfer", (
"Multi-item scoring requires flashinfer attention backend for custom attention mask support. "
f"Please set --attention-backend flashinfer when using --enable-mis. "
f"Current backends: prefill={prefill_backend}, decode={decode_backend}"
)
def _handle_gpu_memory_settings(self, gpu_mem):
"""
Configure GPU memory-dependent settings including
chunked_prefill_size, cuda_graph_max_bs, and mem_fraction_static.
Here are our heuristics:
- Set chunked_prefill_size and cuda_graph_max_bs based on the GPU memory capacity.
This is because GPUs with more memory are generally more powerful, we need to use a larger
chunked_prefill_size and a larger cuda_graph_max_bs to fully utilize the GPU.
- Then set mem_fraction_static based on chunked_prefill_size and cuda_graph_max_bs.
GPU memory capacity = model weights + KV cache pool + activations + cuda graph buffers
The argument mem_fraction_static is defined as (model weights + KV cache pool) / GPU memory capacity,
or equivalently, mem_fraction_static = (GPU memory capacity - activations - cuda graph buffers) / GPU memory capacity.
In order to compute mem_fraction_static, we need to estimate the size of activations and cuda graph buffers.
The activation memory is proportional to the chunked_prefill_size.
The cuda graph memory is proportional to the cuda_graph_max_bs.
We use reserved_mem = chunked_prefill_size * 1.5 + cuda_graph_max_bs * 2 to estimate the size of activations and cuda graph buffers in GB.
and set mem_fraction_static = (GPU memory capacity - reserved_mem) / GPU memory capacity.
The coefficient 1.5 is a heuristic value, in the future, we can do better estimation by looking at the model types, hidden sizes or even do a dummy run.
"""
if gpu_mem is not None:
if gpu_mem < 20 * 1024:
# T4, 4080
# (chunked_prefill_size 2k, cuda_graph_max_bs 8)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 2048
if self.cuda_graph_max_bs is None:
self.cuda_graph_max_bs = 8
elif gpu_mem < 35 * 1024:
# A10, 4090, 5090
# (chunked_prefill_size 2k, cuda_graph_max_bs 24 if tp < 4 else 80)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 2048
if self.cuda_graph_max_bs is None:
# Based on detailed statistics, when serving TP1/TP2 models on lower-end GPUs with HBM < 35GB, you can either disable cuda graph or set `cuda_graph_max_bs` to a very small value to reduce the memory overhead of creating cuda graphs, with almost no impact on performance.
# However, when serving models with TP4 or TP8, we need to enable cuda graph to maintain high performance. In this case, we can set `cuda_graph_max_bs` to 80 (half of the default value 160) to reduce the memory overhead of creating cuda graphs. Looking at the logs
# from TP4 serving of qwen2-72b, a value of 80 is sufficient and can reduce the memory overhead of creating cuda graphs on lower-end GPUs compared to the original 160, avoiding OOM issues.
if self.tp_size < 4:
self.cuda_graph_max_bs = 24
else:
self.cuda_graph_max_bs = 80
elif gpu_mem < 60 * 1024:
# A100 (40GB), L40,
# (chunked_prefill_size 4k, cuda_graph_max_bs 32 if tp < 4 else 160)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 4096
if self.cuda_graph_max_bs is None:
if self.tp_size < 4:
self.cuda_graph_max_bs = 32
else:
self.cuda_graph_max_bs = 160
elif gpu_mem < 90 * 1024:
# H100, A100
# (chunked_prefill_size 8k, cuda_graph_max_bs 256 if tp < 4 else 512)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 8192
if self.cuda_graph_max_bs is None:
if self.tp_size < 4:
self.cuda_graph_max_bs = 256
else:
self.cuda_graph_max_bs = 512
elif gpu_mem < 160 * 1024:
# H20, H200
# (chunked_prefill_size 8k, cuda_graph_max_bs 256 if tp < 4 else 512)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 8192
if self.cuda_graph_max_bs is None:
if self.tp_size < 4:
self.cuda_graph_max_bs = 256
else:
self.cuda_graph_max_bs = 512
else:
# B200, MI300
# (chunked_prefill_size 16k, cuda_graph_max_bs 512)
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 16384
if self.cuda_graph_max_bs is None:
self.cuda_graph_max_bs = 512
else:
# Fallback defaults when gpu_mem is None
if self.chunked_prefill_size is None:
self.chunked_prefill_size = 4096
if self.cuda_graph_max_bs is None:
self.cuda_graph_max_bs = 160
# Set cuda graph batch sizes
if self.device != "cpu":
if self.cuda_graph_bs is None:
self.cuda_graph_bs = self._generate_cuda_graph_batch_sizes()
else:
self.cuda_graph_max_bs = max(self.cuda_graph_bs)
else:
# Reuse cuda_graph_bs for cpu graph and use torch_compile_max_bs for cpu graph batch size limit,
# as cpu graph is based on torch.compile
if self.cuda_graph_bs is not None:
self.torch_compile_max_bs = max(self.cuda_graph_bs)
else:
# If cuda_graph_bs is not set, we will preferentially use torch_compile_max_bs
# to generate cuda_graph_bs
self.torch_compile_max_bs = (
self.torch_compile_max_bs or self.cuda_graph_max_bs
)
self.cuda_graph_bs = self._generate_cpu_graph_batch_sizes()
assert (
self.torch_compile_max_bs > 0
), "cuda_graph_bs should contain positive batch sizes"
self.cuda_graph_max_bs = self.torch_compile_max_bs
if self.piecewise_cuda_graph_max_tokens is None:
# Refer to pr #15927, by default we set the piecewise cuda graph max tokens to the chunked prefill size by default.
# For MLA backend, the introduction of piecewise cuda graph will influence the kernel dispatch difference compared to the original mode.
# To avoid the performance regression, we set the max tokens to 2048 by default.
if not self.use_mla_backend():
self.piecewise_cuda_graph_max_tokens = self.chunked_prefill_size
else:
self.piecewise_cuda_graph_max_tokens = 2048
# If max_total_tokens is set, cap pcg tokens to not exceed max_total_tokens
if self.max_total_tokens is not None:
self.piecewise_cuda_graph_max_tokens = min(
self.piecewise_cuda_graph_max_tokens, self.max_total_tokens
)
# For Llama2 series models, the max tokens is limited to 4096
# TODO(yuwei): remove this after the issue is fixed
if "llama-2" in self.model_path.lower():
self.piecewise_cuda_graph_max_tokens = min(
self.piecewise_cuda_graph_max_tokens, 4096
)
if self.piecewise_cuda_graph_tokens is None:
self.piecewise_cuda_graph_tokens = (
self._generate_piecewise_cuda_graph_tokens()
)
if self.mem_fraction_static is None:
# Constant meta data (e.g., from attention backend)
reserved_mem = 512
# For activation during large prefill
if self.chunked_prefill_size > 0:
reserved_mem += max(self.chunked_prefill_size, 2048) * 1.5
else:
reserved_mem += max(self.max_prefill_tokens, 2048) * 1.5
# For cuda graphs
reserved_mem += self.cuda_graph_max_bs * 2
# Some adjustments for large parallel size
reserved_mem += self.tp_size * self.pp_size / 8 * 1024
if self.enable_dp_attention:
# DP attention needs more padding for some operations
reserved_mem += self.cuda_graph_max_bs * self.dp_size * 3
# DP attention uses much more memory for large cuda graph max bs,
# likely due to some inefficiencies in torch allocator or our implementation.
# So we need to reserve more memory.
if self.cuda_graph_max_bs > 300:
reserved_mem += self.cuda_graph_max_bs * self.dp_size * 1.5
# For piecewise cuda graphs
if not self.disable_piecewise_cuda_graph:
if not self.use_mla_backend():
# Only calculate the memory overhead for Non-Torch Memory use since the Torch Memory can be reused with Cuda Graph Capture
reserved_mem += len(self.piecewise_cuda_graph_tokens) * 8
else:
# For MLA backend the memory overhead is much higher than expected with fa3
reserved_mem += 1.5 * 1024
if gpu_mem is not None and gpu_mem > 60 * 1024:
reserved_mem = max(reserved_mem, 10 * 1024)
if self.speculative_algorithm is not None:
if self.speculative_algorithm == "STANDALONE":
# standalonedraft model and cuda graphs
reserved_mem += 6 * 1024
elif self.speculative_algorithm != "NGRAM":
# eagle draft models and cuda graphs
reserved_mem += 4 * 1024
self.mem_fraction_static = (
round((gpu_mem - reserved_mem) / gpu_mem, 3)
if gpu_mem is not None
else 0.88
)
# Multimodal models need more memory for the image processing,
# so we adjust the mem_fraction_static accordingly.
model_config = self.get_model_config()
if model_config.is_multimodal and not self.language_only:
self.adjust_mem_fraction_for_vlm(model_config)
# If symm mem is enabled and prealloc size is not set, set it to 4GB
if self.enable_symm_mem and not envs.SGLANG_SYMM_MEM_PREALLOC_GB_SIZE.is_set():
envs.SGLANG_SYMM_MEM_PREALLOC_GB_SIZE.set(4)
logger.warning(
"Symmetric memory is enabled, setting symmetric memory prealloc size to 4GB as default."
"Use environment variable SGLANG_SYMM_MEM_PREALLOC_GB_SIZE to change the prealloc size."
)
def _generate_cuda_graph_batch_sizes(self):
"""
Generate the list of batch sizes for CUDA graph capture based on cuda_graph_max_bs.
This integrates the logic from cuda_graph_runner.py.
"""
# Handle disable_cuda_graph_padding as the first condition for both spec and non-spec
if self.disable_cuda_graph_padding:
capture_bs = list(range(1, self.cuda_graph_max_bs + 1))
elif self.speculative_algorithm is None:
# Normal case:
capture_bs = (
[1, 2, 4, 8, 12]
+ list(range(16, 257, 8))
+ list(range(272, 512, 16))
+ list(range(512, self.cuda_graph_max_bs + 1, 32))
)
else:
# Spec decoding case: less padding for smaller batch sizes
capture_bs = (
list(range(1, 9, 1))
+ list(range(10, 33, 2))
+ list(range(40, 65, 4))
+ list(range(72, 257, 8))
+ list(range(272, self.cuda_graph_max_bs + 1, 16))
)
capture_bs = [bs for bs in capture_bs if bs <= self.cuda_graph_max_bs]
if self.cuda_graph_max_bs not in capture_bs:
capture_bs.append(self.cuda_graph_max_bs)
return capture_bs
def _generate_cpu_graph_batch_sizes(self):
"""
Generate the list of batch sizes for CPU graph capture based on torch_compile_max_bs.
"""
if self.disable_cuda_graph_padding:
capture_bs = list(range(1, self.torch_compile_max_bs + 1))
else:
capture_bs = sorted(
set().union(
range(1, 17),
range(18, 31, 2),
range(32, 81, 4),
range(84, self.torch_compile_max_bs + 1, 8),
{self.torch_compile_max_bs},
)
)
capture_bs = [bs for bs in capture_bs if bs <= self.torch_compile_max_bs]
return capture_bs
def _generate_piecewise_cuda_graph_tokens(self):
"""
Generate the list of batch sizes for piecewise CUDA graph capture
based on piecewise_cuda_graph_max_tokens.
"""
capture_sizes = (
list(range(4, 33, 4))
+ list(range(48, 257, 16))
+ list(range(288, 513, 32))
+ list(range(576, 1024 + 1, 64))
+ list(range(1280, 4096 + 1, 256))
+ list(range(4608, self.piecewise_cuda_graph_max_tokens + 1, 512))
)
capture_sizes = [
s for s in capture_sizes if s <= self.piecewise_cuda_graph_max_tokens
]
return capture_sizes
def _set_default_nsa_kv_cache_dtype(self, major: int, quantization: str) -> str:
user_set_prefill = self.nsa_prefill_backend is not None
user_set_decode = self.nsa_decode_backend is not None
# If user specified a backend but didn't explicitly set kv_cache_dtype,
# suggest them to be explicit about kv_cache_dtype to avoid surprises
if (user_set_prefill or user_set_decode) and self.kv_cache_dtype == "auto":
logger.warning(
"When specifying --nsa-prefill-backend or --nsa-decode-backend, "
"you should also explicitly set --kv-cache-dtype (e.g., 'fp8_e4m3' or 'bfloat16'). "
"DeepSeek V3.2 defaults to FP8 KV cache which may not be compatible with all backends."
)
if self.kv_cache_dtype == "auto":
if major >= 10:
self.kv_cache_dtype = "fp8_e4m3"
else:
self.kv_cache_dtype = "bfloat16"
logger.warning(
f"Setting KV cache dtype to {self.kv_cache_dtype} for DeepSeek DSA on SM{major} device."
)
if self.kv_cache_dtype == "bf16":
self.kv_cache_dtype = "bfloat16"
assert self.kv_cache_dtype in [
"bfloat16",
"fp8_e4m3",
], "DeepSeek DSA only supports bf16/bfloat16 or fp8_e4m3 kv_cache_dtype"
def _set_default_nsa_backends(self, kv_cache_dtype: str, major: int) -> str:
user_set_prefill = self.nsa_prefill_backend is not None
user_set_decode = self.nsa_decode_backend is not None
# HiSparse requires flashmla_sparse for both prefill and decode
if self.enable_hisparse:
if not user_set_prefill:
self.nsa_prefill_backend = "flashmla_sparse"
if not user_set_decode:
self.nsa_decode_backend = "flashmla_sparse"
logger.warning(
f"HiSparse enabled: using flashmla_sparse NSA backends "
f"(prefill={self.nsa_prefill_backend}, decode={self.nsa_decode_backend})."
)
return
if not user_set_prefill and not user_set_decode and is_hip():
self.nsa_prefill_backend = "tilelang"
self.nsa_decode_backend = "tilelang"
elif kv_cache_dtype == "fp8_e4m3":
if major >= 10:
if not user_set_prefill:
self.nsa_prefill_backend = "trtllm"
if not user_set_decode:
self.nsa_decode_backend = "trtllm"
else:
# Hopper FP8 defaults to flashmla_kv for both prefill and decode.
if not user_set_prefill:
self.nsa_prefill_backend = "flashmla_kv"
if not user_set_decode:
self.nsa_decode_backend = "flashmla_kv"
else:
# set prefill/decode backends based on hardware architecture.
if major >= 10:
if not user_set_prefill:
self.nsa_prefill_backend = "flashmla_sparse"
if not user_set_decode:
self.nsa_decode_backend = "trtllm"
else:
# Hopper defaults for bfloat16
if not user_set_prefill:
self.nsa_prefill_backend = "flashmla_sparse"
if not user_set_decode:
self.nsa_decode_backend = "fa3"
logger.warning(
f"Set NSA backends for {self.kv_cache_dtype} KV Cache: prefill={self.nsa_prefill_backend}, decode={self.nsa_decode_backend}."
)
def _handle_model_specific_adjustments(self):
from sglang.srt.configs.model_config import is_deepseek_nsa
if parse_connector_type(self.model_path) == ConnectorType.INSTANCE:
return
hf_config = self.get_model_config().hf_config
model_arch = hf_config.architectures[0]
_hybrid_spec = get_linear_attn_spec_by_arch(model_arch)
if _hybrid_spec is not None:
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=_hybrid_spec.support_mamba_cache,
support_mamba_cache_extra_buffer=_hybrid_spec.support_mamba_cache_extra_buffer,
)
if model_arch in [
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
]:
self.dtype = "bfloat16"
if model_arch in [
"DeepseekV3ForCausalLM",
"KimiK25ForConditionalGeneration",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
"GlmMoeDsaForCausalLM",
]:
# Set attention backend for DeepSeek
if is_deepseek_nsa(hf_config): # DeepSeek 3.2/GLM 5
if model_arch == "GlmMoeDsaForCausalLM" and is_blackwell_supported():
envs.SGLANG_NSA_PREFILL_DENSE_ATTN_KV_LEN_THRESHOLD.set(0)
logger.warning(
"Force NSA prefill to use sparse MLA (i.e. disable MHA_ONE_SHOT) for GlmMoeDsaForCausalLM on Blackwell."
)
else:
if envs.SGLANG_NSA_PREFILL_DENSE_ATTN_KV_LEN_THRESHOLD.is_set():
logger.warning(
f"Dense attention kv len threshold is manually set to {envs.SGLANG_NSA_PREFILL_DENSE_ATTN_KV_LEN_THRESHOLD.get()} for DSA. Caution: This may cause performance regression if the threshold is larger than the index topk of model."
)
else:
# When threshold is not manually set, set it to the index topk of model
from sglang.srt.configs.model_config import get_nsa_index_topk
envs.SGLANG_NSA_PREFILL_DENSE_ATTN_KV_LEN_THRESHOLD.set(
get_nsa_index_topk(hf_config)
)
logger.warning(
f"Set dense attention kv len threshold to model index_topk={envs.SGLANG_NSA_PREFILL_DENSE_ATTN_KV_LEN_THRESHOLD.get()} for DeepSeek with DSA."
)
if self.is_attention_backend_not_set():
self.attention_backend = "nsa"
logger.info("Use nsa attention backend for DeepSeek with DSA.")
if not is_npu(): # CUDA or ROCm GPU
if self.enable_nsa_prefill_context_parallel:
logger.warning(
"Context parallel feature is still under experiment. It has only been verified on Hopper platform."
)
if self.nsa_prefill_cp_mode == "in-seq-split":
# TODO Supports moe_dense_tp_size != 1, kv cache dtype = "fp8",moe_a2a_backend non-deepep and cross-machine operation .
self.enable_dp_attention = True
self.moe_dense_tp_size = 1
self.moe_a2a_backend = "deepep"
self.ep_size = self.tp_size
logger.warning(
"For in-seq split mode, we have the following restrictions: moe_dense_tp_size == 1, moe_a2a_backend == deepep, ep_size == tp_size, batch_size == 1"
)
else:
self.enable_dp_attention = True
self.moe_dense_tp_size = 1
assert (
self.dp_size == 1
), "For round-robin split mode, dp attention is not supported."
assert (
self.tp_size == 8
), "Current multi-machine CP support suffers from precision issues. So context parallel only support Single machine(tp_size == 8)"
self.attn_cp_size = self.tp_size // self.dp_size
logger.warning(
f"Enable Context Parallel opt for deeeseekv3.2-DSA, Setting dp_size == {self.dp_size} and moe_dense_tp_size == {self.moe_dense_tp_size}, ep_size == {self.ep_size}, tp_size == {self.tp_size}, kv_cache_dtype == {self.kv_cache_dtype}, moe_a2a_backend {self.moe_a2a_backend} "
)
else:
# Pure TP and partial DP Attention mode is active for NSA, logging a warning
if self.dp_size < self.tp_size:
logger.warning(
f"DSA with TP mode is active, dp_size={self.dp_size}, tp_size={self.tp_size}, "
f"attn_tp_size={self.tp_size}, attention weights will be sharded across {self.tp_size} ranks."
)
if is_hip():
self.page_size = 1
logger.warning(
"Setting page size to 1 for DeepSeek DSA on ROCm."
)
else:
# For CUDA GPU
self.page_size = 64
logger.warning("Setting page size to 64 for DeepSeek DSA.")
import torch
major, _ = torch.cuda.get_device_capability()
self._set_default_nsa_kv_cache_dtype(major, self.quantization)
self._set_default_nsa_backends(self.kv_cache_dtype, major)
if self.enable_nsa_prefill_context_parallel:
assert (
self.disaggregation_mode != "decode"
), "CP is only supported for prefill when PD disaggregation, please remove --enable-nsa-prefill-context-parallel."
else:
# DeepSeek V3/R1/V3.1
if not self.disable_piecewise_cuda_graph:
logger.info("Piecewise CUDA graph is enabled, use MLA for prefill.")
if is_sm100_supported():
if (
self.attention_backend is None
and self.prefill_attention_backend is None
and self.decode_attention_backend is None
):
self.attention_backend = "trtllm_mla"
logger.info(
"Use trtllm_mla as attention backend on sm100 for DeepseekV3ForCausalLM"
)
# Set moe backend for DeepSeek
if is_sm100_supported():
quant_method = get_quantization_config(hf_config)
quant_cfg = getattr(hf_config, "quantization_config", None) or {}
config_groups = quant_cfg.get("config_groups", {})
group0 = config_groups.get("group_0", {})
weights_cfg = group0.get("weights", {})
# this also apply to kimi k2.5
# since it follow the compressed tensor int4 recipe
# but not kimi k2 instruct or 0905 instruct.
is_kimi_k2_k25_thinking_int4 = (
quant_method == "compressed-tensors"
and weights_cfg.get("num_bits") == 4
and weights_cfg.get("group_size") == 32
and weights_cfg.get("strategy") == "group"
and weights_cfg.get("type") == "int"
)
if (
self.quantization is None
and not self._quantization_explicitly_unset
):
# DeepSeek V3/R1 uses native FP8 MoE experts without
# declaring it in quantization_config. However, other
# models that share the same architecture class (e.g.
# Moonlight-16B-A3B) are purely BF16. Check the actual
# safetensors header instead of assuming FP8 by arch name.
if quant_method is None and model_arch in ["DeepseekV3ForCausalLM"]:
if has_fp8_weights_in_checkpoint(self.model_path):
self.quantization = "fp8"
logger.info(
"Detected FP8 expert weights in checkpoint, "
"default to fp8 for DeepSeek on sm100"
)
else:
logger.info(
"No FP8 expert weights found in checkpoint, "
"keeping bf16 for DeepSeek-arch model on sm100"
)
else:
self.quantization = quant_method
if (
self.moe_a2a_backend == "none"
and self.moe_runner_backend == "auto"
and (
self.quantization
in ["fp8", "modelopt_fp8", "modelopt_fp4", "modelopt_mixed"]
or is_kimi_k2_k25_thinking_int4
)
):
self.moe_runner_backend = "flashinfer_trtllm"
if is_kimi_k2_k25_thinking_int4:
logger.info(
"Use flashinfer_trtllm as MoE runner backend on Blackwell for Kimi K2 / K2.5 thinking int4"
)
else:
logger.info(
"Use flashinfer_trtllm as MoE runner backend on sm100 for DeepseekV3ForCausalLM"
)
elif is_hip():
if not self.enable_dp_attention and self.nnodes == 1:
# TODO (Hubert): Put this back later
# self.enable_aiter_allreduce_fusion = True
logger.info(
"Enable Aiter AllReduce Fusion for DeepseekV3ForCausalLM"
)
if (
self.quantization == "modelopt_fp4"
and self.speculative_algorithm == "EAGLE"
and (
self.speculative_moe_runner_backend is None
or self.speculative_moe_a2a_backend is None
)
):
if envs.SGLANG_NVFP4_CKPT_FP8_NEXTN_MOE.get():
self.speculative_moe_runner_backend = "deep_gemm"
self.speculative_moe_a2a_backend = "deepep"
logger.info(
"Use deep_gemm moe runner and deepep a2a backend for bf16 nextn layer in deepseek fp4 checkpoint."
)
# Validate usage of ep
if self.ep_size == 1:
raise ValueError(
"Invalid configuration: 'deep_gemm' speculative MoE runner backend with "
"'deepep' a2a backend requires expert parallelism (ep_size > 1). "
f"Current ep_size is {self.ep_size}. "
"Please set --ep-size > 1 (e.g., --ep-size 8) to use this configuration, "
"or change --speculative-moe-a2a-backend to 'none' if expert parallelism is not available."
)
else:
self.speculative_moe_runner_backend = "triton"
self.speculative_moe_a2a_backend = "none"
logger.info(
"Use triton fused moe by default for bf16 nextn layer in deepseek fp4 checkpoint."
)
elif model_arch in ["GptOssForCausalLM"]:
# Set attention backend for GPT-OSS
if self.is_attention_backend_not_set():
if is_sm100_supported():
self.attention_backend = "trtllm_mha"
elif is_sm90_supported():
self.attention_backend = "fa3"
elif is_xpu():
self.attention_backend = "intel_xpu"
elif is_hip():
self.attention_backend = "aiter"
else:
self.attention_backend = "triton"
if is_xpu():
# Check for bf16 dtype on Intel XPU
if self.dtype == "auto":
logger.warning(
"GptOssForCausalLM on Intel XPU currently supports bfloat16 dtype only"
)
elif self.dtype not in ["bfloat16"]:
raise NotImplementedError(
f"GptOssForCausalLM on Intel XPU only supports bfloat16 dtype, "
f"but got '{self.dtype}'. Please use --dtype bfloat16 or remove --dtype to use auto."
)
supported_backends = [
"triton",
"trtllm_mha",
"fa3",
"fa4",
"ascend",
"intel_xpu",
"aiter",
]
prefill_attn_backend, decode_attn_backend = self.get_attention_backends()
assert (
prefill_attn_backend in supported_backends
and decode_attn_backend in supported_backends
), (
f"GptOssForCausalLM requires one of {supported_backends} attention backend, but got the following backends\n"
f"- Prefill: {prefill_attn_backend}\n"
f"- Decode: {decode_attn_backend}\n"
)
quant_method = get_quantization_config(hf_config)
is_mxfp4_quant_format = quant_method == "mxfp4"
if not self.enable_dp_attention and self.nnodes == 1 and is_hip():
# TODO (Hubert): Put this back later
# self.enable_aiter_allreduce_fusion = True
logger.info("Enable Aiter AllReduce Fusion 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_mxfp4_quant_format:
# use bf16 for mxfp4 triton kernels
self.dtype = "bfloat16"
if self.moe_runner_backend == "auto":
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."
)
elif is_sm120_supported() and is_mxfp4_quant_format:
# trtllm-gen only supports SM100
self.moe_runner_backend = "triton_kernel"
logger.warning(
"Detected SM120 and MXFP4 quantization format for GPT-OSS model, enabling triton_kernel MOE kernel."
)
elif (
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 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"
logger.warning(
"Detected ROCm with SGLANG_USE_AITER for GPT-OSS bf16 model, using triton MOE kernel."
)
elif (
self.ep_size == 1
and is_triton_kernels_available()
and self.quantization is None
):
# The triton_kernels package segfaults on Blackwell (B200)
# with NVIDIA driver >= 595. Fall back to triton backend.
if is_blackwell_supported() and get_nvidia_driver_version() >= (
595,
):
self.moe_runner_backend = "triton"
logger.warning(
"Detected GPT-OSS model on Blackwell with driver >= 595, "
"using triton MOE kernel to avoid triton_kernels SIGSEGV."
)
else:
self.moe_runner_backend = "triton_kernel"
logger.warning(
"Detected GPT-OSS model, enabling triton_kernels MOE kernel."
)
if self.moe_runner_backend == "triton_kernel":
assert (
self.ep_size == 1
), "Triton kernel MoE is only supported when ep_size == 1"
elif any(
x in model_arch
for x in (
"MiMoV2ForCausalLM",
"MiMoV2FlashForCausalLM",
)
):
if self.speculative_algorithm == "EAGLE":
self.enable_multi_layer_eagle = True
logger.info(
"Enable multi-layer EAGLE speculative decoding for MiMoV2 model."
)
if not envs.SGLANG_ENABLE_SPEC_V2.get():
envs.SGLANG_ENABLE_SPEC_V2.set(True)
logger.warning(
"Spec v2 is enabled for multi-layer EAGLE speculative decoding."
)
if self.enable_hierarchical_cache:
self.swa_full_tokens_ratio = 1.0
logger.warning(
"Reset swa_full_tokens_ratio to 1.0 for MiMoV2 model with hierarchical cache"
)
self.disable_hybrid_swa_memory = True
logger.warning(
"Disable hybrid SWA memory for MiMoV2 model with hierarchical cache"
)
elif "Step3p5ForCausalLM" in model_arch:
if self.speculative_algorithm == "EAGLE":
self.enable_multi_layer_eagle = True
logger.info(
"Enable multi-layer EAGLE speculative decoding for Step3p5ForCausalLM model."
)
if not envs.SGLANG_ENABLE_SPEC_V2.get():
envs.SGLANG_ENABLE_SPEC_V2.set(True)
logger.warning(
"Spec v2 is enabled for multi-layer EAGLE speculative decoding."
)
if self.enable_hierarchical_cache:
self.swa_full_tokens_ratio = 1.0
logger.warning(
"Reset swa_full_tokens_ratio to 1.0 for Step3p5ForCausalLM model with hierarchical cache"
)
self.disable_hybrid_swa_memory = True
logger.warning(
"Disable hybrid SWA memory for Step3p5ForCausalLM model with hierarchical cache"
)
elif "Llama4" in model_arch and self.device != "cpu":
# Auto-select attention backend for Llama4 if not specified
if self.attention_backend is None:
if is_sm100_supported():
self.attention_backend, platform = "trtllm_mha", "sm100"
elif is_sm90_supported():
self.attention_backend, platform = "fa3", "sm90"
elif is_hip():
self.attention_backend, platform = "aiter", "hip"
elif self.device == "xpu":
self.attention_backend, platform = "intel_xpu", "xpu"
else:
self.attention_backend, platform = "triton", "other platforms"
logger.warning(
f"Use {self.attention_backend} as attention backend on {platform} for Llama4 model"
)
assert self.attention_backend in {
"fa3",
"aiter",
"triton",
"ascend",
"trtllm_mha",
"intel_xpu",
}, f"fa3, aiter, triton, ascend, trtllm_mha or intel_xpu is required for Llama4 model but got {self.attention_backend}"
if is_sm100_supported() and self.moe_runner_backend == "auto":
if self.quantization in {"fp8", "modelopt_fp8"}:
self.moe_runner_backend = "flashinfer_trtllm"
logger.info(
"Use flashinfer_trtllm as MoE runner backend on SM100 for Llama4"
)
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
elif model_arch == "Gemma4ForConditionalGeneration":
if self.is_attention_backend_not_set():
self.attention_backend = "triton"
logger.info("Use triton as default attention backend for Gemma4")
elif model_arch == "MossVLForConditionalGeneration":
if self.is_attention_backend_not_set():
self.prefill_attention_backend = "flashinfer"
logger.info(
"Use flashinfer as default prefill attention backend for Moss-VL"
)
prefill_backend, _ = self.get_attention_backends()
assert prefill_backend == "flashinfer", (
"MossVLForConditionalGeneration requires flashinfer prefill "
"attention backend for cross-attention custom mask support."
)
elif model_arch in ["Exaone4ForCausalLM", "ExaoneMoEForCausalLM"]:
if hf_config.sliding_window_pattern is not None:
logger.warning(
f"Disabling hybrid SWA memory for {model_arch} as it is not yet supported."
)
self.disable_hybrid_swa_memory = True
# https://docs.sglang.ai/advanced_features/attention_backend.html
accepted_backends = ["fa3", "triton", "trtllm_mha"]
assert (
self.attention_backend in accepted_backends
), f"One of the attention backends in {accepted_backends} is required for {model_arch}, but got {self.attention_backend}"
elif model_arch in ["Olmo2ForCausalLM"]:
# FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with Olmo3 model.
logger.warning(
f"Disabling hybrid SWA memory for {model_arch} as it is not yet supported."
)
self.disable_hybrid_swa_memory = True
if self.attention_backend is None:
if is_cuda() and is_sm100_supported():
self.attention_backend = "trtllm_mha"
elif is_cuda() and get_device_sm() >= 80:
self.attention_backend = "fa3"
else:
self.attention_backend = "triton"
# Flashinfer appears to degrade performance when sliding window attention
# is used for the Olmo2 architecture. Olmo2 does not use sliding window attention
# but Olmo3 does.
assert (
self.attention_backend != "flashinfer"
), "FlashInfer backend can significantly degrade the performance of Olmo3 models."
logger.info(
f"Using {self.attention_backend} as attention backend for {model_arch}."
)
elif model_arch in ["KimiLinearForCausalLM", "BailingMoeV2_5ForCausalLM"]:
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=False,
)
elif model_arch in ["NemotronHForCausalLM"]:
model_config = self.get_model_config()
if model_config.quantization in [
"modelopt",
"modelopt_fp8",
"modelopt_fp4",
"modelopt_mixed",
]:
assert model_config.hf_config.mlp_hidden_act == "relu2"
if model_config.quantization == "modelopt":
quant_algo = model_config.hf_config.quantization_config[
"quant_algo"
]
if quant_algo == "MIXED_PRECISION":
self.quantization = "modelopt_mixed"
else:
self.quantization = (
"modelopt_fp4" if quant_algo == "NVFP4" else "modelopt_fp8"
)
else:
self.quantization = model_config.quantization
self.moe_runner_backend = "flashinfer_cutlass"
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=True,
support_mamba_cache_extra_buffer=False,
sm100_default_attention_backend="flashinfer",
)
assert self.attention_backend != "triton", (
"NemotronHForCausalLM does not support triton attention backend,"
"as the first layer might not be an attention layer"
)
elif model_arch in [
"Qwen3MoeForCausalLM",
"Qwen3VLMoeForConditionalGeneration",
"Qwen3NextForCausalLM",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
]:
if is_sm100_supported():
quant_method = get_quantization_config(hf_config)
if (
self.quantization is None
and not self._quantization_explicitly_unset
and quant_method is not None
):
self.quantization = quant_method
if (
(
self.quantization in ("fp8", "modelopt_fp4")
or self.quantization is None
)
and self.moe_a2a_backend == "none"
and self.moe_runner_backend == "auto"
):
self.moe_runner_backend = "flashinfer_trtllm"
logger.info(
"Use flashinfer_trtllm as MoE runner backend on sm100 for "
f"{model_arch}"
)
if model_arch in [
"Qwen3NextForCausalLM",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
]:
sm100_default_attn_backend = "triton"
if is_sm100_supported():
# trtllm_mha requires speculative_eagle_topk == 1 and page_size > 1.
# _get_default_attn_backend handles the eagle_topk check.
# There is only one case where page_size=1 is required,
# which is when radix cache is enabled and both extra_buffer
# and spec decoding are disabled.
default_attn_backend = self._get_default_attn_backend(
use_mla_backend=self.use_mla_backend(),
model_config=self.get_model_config(),
)
if default_attn_backend == "trtllm_mha" and not (
not self.enable_mamba_extra_buffer()
and not self.disable_radix_cache
and self.speculative_algorithm is None
):
sm100_default_attn_backend = "trtllm_mha"
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=True,
support_mamba_cache_extra_buffer=True,
sm100_default_attention_backend=sm100_default_attn_backend,
)
elif model_arch in ["Glm4MoeForCausalLM"]:
if is_sm100_supported():
quantization_config = getattr(hf_config, "quantization_config", None)
quant_method = (
quantization_config.get("quant_method")
if quantization_config is not None
else None
)
if (
self.quantization is None
and not self._quantization_explicitly_unset
and quant_method is not None
):
self.quantization = quant_method
if (
self.quantization == "modelopt_fp4"
and self.moe_a2a_backend == "none"
and self.moe_runner_backend == "auto"
):
self.moe_runner_backend = "flashinfer_trtllm"
logger.info(
"Use flashinfer_trtllm as MoE runner backend on sm100 for Glm4MoeForCausalLM"
)
elif model_arch in [
"FalconH1ForCausalLM",
"JetNemotronForCausalLM",
"JetVLMForConditionalGeneration",
]:
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=True,
support_mamba_cache_extra_buffer=False,
sm100_default_attention_backend="triton",
)
elif model_arch == "GraniteMoeHybridForCausalLM":
hf_config = self.get_model_config().hf_config
has_mamba = any(
layer_type == "mamba"
for layer_type in getattr(hf_config, "layer_types", [])
)
if has_mamba:
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache_extra_buffer=False,
sm100_default_attention_backend="triton",
)
elif model_arch in ["Lfm2ForCausalLM"]:
self._handle_mamba_radix_cache(
model_arch=model_arch,
support_mamba_cache=True,
support_mamba_cache_extra_buffer=False,
sm100_default_attention_backend="flashinfer",
)
assert self.attention_backend != "triton", (
f"{model_arch} does not support triton attention backend, "
"as the first layer might not be an attention layer"
)
if (
model_arch in ["Qwen3VLForConditionalGeneration"]
and is_hip()
and envs.SGLANG_USE_AITER_UNIFIED_ATTN.get()
and self.page_size is None
):
self.page_size = 16
logger.info(
"Setting page_size=16 for aiter unified attention on Qwen3VLForConditionalGeneration."
)
if envs.SGLANG_EMBEDDINGS_SPARSE_HEAD.is_set():
self.disable_overlap_schedule = True
logger.warning(
"Overlap scheduler is disabled when using sparse head for embedding model."
)
# TRTLLM AllReduce Fusion supports SM90/100, enable it by default
# for models with explicit support (DeepseekV3, GptOss, Glm4Moe,
# Qwen3/Qwen3Next/Qwen3.5 MoE families)
# TODO: currently, it is only supported in the single node scenario. https://github.com/flashinfer-ai/flashinfer/issues/2006
# TODO: there is currently a bug on H20 device specifically, https://github.com/flashinfer-ai/flashinfer/issues/2204
device_name = get_device_name()
is_h20_device = (
device_name and "H20" in device_name and "H200" not in device_name
)
if (
not self.enable_flashinfer_allreduce_fusion
and model_arch
in [
"DeepseekV3ForCausalLM",
"DeepseekV32ForCausalLM",
"GptOssForCausalLM",
"GlmMoeDsaForCausalLM",
"Glm4MoeForCausalLM",
"Glm4MoeLiteForCausalLM",
"Qwen3MoeForCausalLM",
"Qwen3NextForCausalLM",
"KimiK25ForConditionalGeneration",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
]
and (is_sm90_supported() or is_sm100_supported())
and self.tp_size > 1
and not self.enable_dp_attention
and self.nnodes == 1
and not is_h20_device
and self.moe_a2a_backend == "none"
):
self.enable_flashinfer_allreduce_fusion = True
logger.info(
f"Auto-enabling FlashInfer AllReduce Fusion on SM90/SM10X for {model_arch}"
)
# Apply enforce_disable_flashinfer_allreduce_fusion after all model-specific adjustments
if self.enforce_disable_flashinfer_allreduce_fusion:
self.enable_flashinfer_allreduce_fusion = False
logger.info(
"FlashInfer allreduce fusion is forcibly disabled "
"via --enforce-disable-flashinfer-allreduce-fusion."
)
def _handle_mamba_radix_cache(
self,
model_arch: str,
support_mamba_cache: bool = True,
support_mamba_cache_extra_buffer: bool = True,
sm100_default_attention_backend: str = None,
):
if (
is_sm100_supported()
and self.attention_backend is None
and sm100_default_attention_backend is not None
):
self.attention_backend = sm100_default_attention_backend
logger.info(
f"Use {sm100_default_attention_backend} as attention backend on sm100 for {model_arch}"
)
if not support_mamba_cache:
logger.warning(
f"Disabling Radix Cache for {model_arch} as it is not yet supported."
)
self.disable_radix_cache = True
return
if not support_mamba_cache_extra_buffer:
assert (
not self.enable_mamba_extra_buffer()
), f"mamba extra_buffer is not supported for {model_arch} model"
if self.enable_mamba_extra_buffer(): # extra_buffer
if self.disable_radix_cache:
raise ValueError(
"mamba extra_buffer is not compatible with --disable-radix-cache "
"Overlap scheduling is already supported with no_buffer + disable_radix_cache. "
"Please use --mamba-scheduler-strategy no_buffer instead."
)
assert (
is_cuda()
), "Mamba extra_buffer is only supported on CUDA devices with FLA backend"
if self.speculative_num_draft_tokens is not None:
assert (
self.mamba_track_interval >= self.speculative_num_draft_tokens
), f"mamba_track_interval {self.mamba_track_interval} must be greater than or equal to speculative_num_draft_tokens {self.speculative_num_draft_tokens}"
if self.page_size is not None:
assert (
self.mamba_track_interval % self.page_size == 0
), f"mamba_track_interval {self.mamba_track_interval} must be divisible by page_size {self.page_size}"
assert (
max(FLA_CHUNK_SIZE, self.page_size)
% min(FLA_CHUNK_SIZE, self.page_size)
== 0
), f"For SSM models with extra buffer, either FLA_CHUNK_SIZE or page_size must be divisible by the other, got {FLA_CHUNK_SIZE=}, {self.page_size=}"
elif not self.disable_radix_cache: # no_buffer
if self.page_size is not None and self.page_size != 1:
logger.warning(
f"{model_arch} with radix cache requires page_size=1 in the current "
f"Mamba scheduling mode (no_buffer), but got {self.page_size}. "
"Automatically setting page_size=1."
)
self.page_size = 1
if self.speculative_algorithm is None:
logger.warning(
"Disabling overlap schedule since mamba no_buffer is not compatible with "
"overlap schedule, try to use --disable-radix-cache if overlap schedule is necessary"
)
self.disable_overlap_schedule = True
if self.attention_backend == "trtllm_mha":
logger.warning(
"Disabling radix cache since trtllm_mha does not support page_size = 1, which is required by MambaRadixCache. "
"Try to use --attention-backend triton if radix cache is necessary."
)
self.disable_radix_cache = True
self.disable_overlap_schedule = False
else:
if not self.disable_radix_cache:
if is_hip():
# On ROCm, extra_buffer is unsupported.
# Automatically disable radix cache instead.
logger.warning(
f"Speculative decoding for {model_arch} is not compatible "
"with radix cache on ROCm devices. "
"Automatically disabling radix cache."
)
self.disable_radix_cache = True
else:
raise ValueError(
f"Speculative decoding for {model_arch} is not compatible with radix cache when using --mamba-scheduler-strategy no_buffer."
"To use radix cache with speculative decoding, please use --mamba-scheduler-strategy extra_buffer and set SGLANG_ENABLE_SPEC_V2=1."
)
def _handle_sampling_backend(self):
if self.sampling_backend is None:
self.sampling_backend = (
"flashinfer" if is_flashinfer_available() else "pytorch"
)
def _get_default_attn_backend(self, use_mla_backend: bool, model_config):
"""
Auto select the fastest attention backend.
1. Models with MHA Architecture (e.g: Llama, QWen)
1.1 We will turn on FA3 on hopper unless user use spec decode with topk > 1 or page_size > 1.
1.2 Use trtllm_mha for SM100/SM103 (Blackwell B200/GB200/B300) excluding spec with topk > 1.
Note: trtllm_mha does not support SM120, which will fall back to flashinfer.
1.3 In other cases, we will use flashinfer if available, otherwise use triton.
2. Models with MLA Architecture and using FA3
2.1 We will use FA3 backend on hopper.
2.2 We will use Flashinfer backend on blackwell.
2.3 Otherwise, we will use triton backend.
"""
# OOT platforms provide their own default attention backend.
from sglang.srt.platforms import current_platform
if current_platform.is_out_of_tree():
return current_platform.get_default_attention_backend()
# Whisper requires flashinfer for cross-attention CUDA graph support.
if "WhisperForConditionalGeneration" in (
model_config.hf_config.architectures or []
):
return "flashinfer"
if not use_mla_backend:
# MHA architecture
if is_hopper_with_cuda_12_3() and is_no_spec_infer_or_topk_one(self):
# Note: flashinfer 0.6.1 caused performance regression on Hopper attention kernel
# Before the kernel is fixed, we choose fa3 as the default backend on Hopper MHA
# ref: https://github.com/sgl-project/sglang/issues/17411
return "fa3"
elif (
is_sm100_supported()
and is_no_spec_infer_or_topk_one(self)
and (
self.speculative_algorithm is None
or self.speculative_eagle_topk is not None
)
):
return "trtllm_mha"
elif is_hip():
return "aiter"
elif is_mps():
return "torch_native"
else:
# FlashInfer does not support attention sinks.
if is_flashinfer_available() and not model_config.has_attention_sinks:
return "flashinfer"
return "triton"
else:
# MLA architecture
if is_hopper_with_cuda_12_3():
return "fa3"
elif is_sm100_supported():
return "flashinfer"
elif is_hip():
head_num = model_config.get_num_kv_heads(self.tp_size)
# TODO current aiter only support head number 16 or 128 head number
if head_num == 128 or head_num == 16:
return "aiter"
else:
return "triton"
elif is_mps():
return "torch_native"
else:
return "triton"
def _handle_attention_backend_compatibility(self):
model_config = self.get_model_config()
use_mla_backend = self.use_mla_backend()
if self.prefill_attention_backend is not None and (
self.prefill_attention_backend == self.decode_attention_backend
): # override the default attention backend
self.attention_backend = self.prefill_attention_backend
# Pick the default attention backend if not specified
if self.attention_backend is None:
self.attention_backend = self._get_default_attn_backend(
use_mla_backend, model_config
)
logger.info(
f"Attention backend not specified. Use {self.attention_backend} backend by default."
)
# Torch native and flex attention backends
if self.attention_backend == "torch_native":
logger.warning(
"Cuda graph is disabled because of using torch native attention backend"
)
self.disable_cuda_graph = True
if self.attention_backend == "flex_attention":
logger.warning(
"Cuda graph is disabled because of using torch Flex Attention backend"
)
self.disable_cuda_graph = True
assert (
self.speculative_algorithm is None
), "Speculative decoding is currently not supported with Flex Attention backend"
# Whisper's encoder token padding conflicts with prefix caching.
# Only disable for Whisper; other encoder-decoder models (e.g., mllama) use radix cache.
if (
model_config.is_encoder_decoder
and not self.disable_radix_cache
and "WhisperForConditionalGeneration"
in (model_config.hf_config.architectures or [])
):
logger.info("Radix cache is disabled for Whisper")
self.disable_radix_cache = True
# Major NVIDIA platforms backends
if (
self.attention_backend == "flashmla"
or self.decode_attention_backend == "flashmla"
):
logger.warning(
"FlashMLA only supports a page_size of 64, change page_size to 64."
)
self.page_size = 64
if (
self.attention_backend == "cutlass_mla"
or self.decode_attention_backend == "cutlass_mla"
):
logger.warning(
"Cutlass MLA only supports a page_size of 128, change page_size to 128."
)
self.page_size = 128
if (
self.attention_backend == "trtllm_mla"
or self.decode_attention_backend == "trtllm_mla"
):
if not is_blackwell_supported():
raise ValueError(
"TRTLLM MLA backend is only supported on Blackwell GPUs (SM100/SM12x). Please use a different backend."
)
if self.page_size not in [32, 64]:
logger.warning(
f"TensorRT-LLM MLA only supports page_size of 32 or 64, changing page_size from {self.page_size} to 64."
)
self.page_size = 64
if self.kv_cache_dtype not in ["fp8_e4m3", "fp4_e2m1", "bf16", "auto"]:
raise ValueError(
"TensorRT-LLM MLA backend only supports kv-cache-dtype of fp8_e4m3, fp4_e2m1, bf16, or auto."
)
if (
self.attention_backend == "trtllm_mha"
or self.decode_attention_backend == "trtllm_mha"
or self.prefill_attention_backend == "trtllm_mha"
):
# Check prefill backend
prefill_backend = (
self.prefill_attention_backend
if self.prefill_attention_backend is not None
else self.attention_backend
)
if prefill_backend == "trtllm_mha" and not is_sm100_supported():
raise ValueError(
"TRTLLM MHA backend for prefill is only supported on Blackwell GPUs (SM100). Please use a different prefill backend."
)
# Check decode backend
decode_backend = (
self.decode_attention_backend
if self.decode_attention_backend is not None
else self.attention_backend
)
if decode_backend == "trtllm_mha" and not (
is_sm90_supported() or is_sm100_supported() or is_sm120_supported()
):
raise ValueError(
"TRTLLM MHA backend for decode is only supported on Hopper (SM90), Blackwell (SM100) and (SM120) GPUs. Please use a different decode backend."
)
if self.page_size not in [16, 32, 64]:
logger.warning(
f"TensorRT-LLM MHA only supports page_size of 16, 32 or 64, changing page_size from {self.page_size} to 64."
)
self.page_size = 64
if self.attention_backend == "fa3" and self.kv_cache_dtype == "fp8_e5m2":
logger.warning(
"FlashAttention3 only supports fp8_e4m3 if using FP8; "
"Setting attention backend to triton."
)
self.attention_backend = "triton"
if (
self.prefill_attention_backend == "fa4"
and not self.use_mla_backend()
and is_sm100_supported()
):
logger.warning(
f"FA4 backend only supports page size 128 for non-MLA model architectures, changing page_size from {self.page_size} to 128."
)
self.page_size = 128
# AMD platforms backends
if self.attention_backend == "aiter":
if model_config.context_len > 8192:
self.mem_fraction_static *= 0.85
# Other platforms backends
if (
self.attention_backend == "intel_amx"
and self.device == "cpu"
and not cpu_has_amx_support()
):
logger.warning(
"The current platform does not support Intel AMX, will fallback to torch_native backend."
)
self.attention_backend = "torch_native"
if (
self.attention_backend == "intel_xpu"
and self.device == "xpu"
and not xpu_has_xmx_support()
):
logger.warning(
"The current platform does not support Intel XMX, will fallback to triton backend."
)
self.attention_backend = "triton"
if self.attention_backend == "intel_xpu":
if self.page_size not in [32, 64, 128]:
logger.warning(
f"Intel XPU attention backend only supports page_size of 32, 64 or 128, changing page_size from {self.page_size} to 128."
)
self.page_size = 128
# Dual chunk flash attention backend
if (
getattr(model_config.hf_config, "dual_chunk_attention_config", None)
is not None
):
if self.attention_backend is None:
self.attention_backend = "dual_chunk_flash_attn"
logger.info("Dual chunk attention is turned on by default.")
elif self.attention_backend != "dual_chunk_flash_attn":
raise ValueError(
"Dual chunk attention is enabled, but attention backend is set to "
f"{self.attention_backend}. Please set it to 'dual_chunk_flash_attn'."
)
if self.attention_backend == "dual_chunk_flash_attn":
logger.warning(
"Mixed chunk and radix cache are disabled when using dual-chunk flash attention backend"
)
self.enable_mixed_chunk = False
self.disable_radix_cache = True
def _handle_kv4_compatibility(self):
"""Check FP4 KV cache compatibility with the attention backend"""
if self.kv_cache_dtype != "fp4_e2m1":
return
use_mla_backend = self.use_mla_backend()
# self.attention_backend didn't overwrite self.prefill/decode_attention_backend yet
self.prefill_attention_backend_str, self.decode_attention_backend_str = (
self.get_attention_backends()
)
if is_cuda():
if (
self.prefill_attention_backend_str != self.decode_attention_backend_str
and self.prefill_attention_backend_str != "fa4"
): # Take care of prefill=fa4 later
logger.warning(
f"Attention: Using KV4 with PREFILL = {self.prefill_attention_backend_str} "
f"and DECODE = {self.decode_attention_backend_str}. "
f"Compatibility issues are unlikely, but may occur in rare edge cases."
)
else:
if self.prefill_attention_backend_str == "fa4":
if use_mla_backend: # FA4 + MLA
KV4_FA4_MLA_BACKEND_CHOICES = [
"cutlass_mla",
"flashinfer",
"trtllm_mla",
]
assert (
self.decode_attention_backend_str
in KV4_FA4_MLA_BACKEND_CHOICES
), (
f"KV4 FA4 MLA expects decode_attention_backend to be one of "
f"{KV4_FA4_MLA_BACKEND_CHOICES}, but got {self.decode_attention_backend_str}"
)
else: # FA4 + MHA
KV4_FA4_MHA_BACKEND_CHOICES = [
"triton",
"torch_native",
"flex_attention",
]
assert (
self.decode_attention_backend_str
in KV4_FA4_MHA_BACKEND_CHOICES
), (
f"KV4 FA4 MHA expects decode_attention_backend to be one of "
f"{KV4_FA4_MHA_BACKEND_CHOICES}, but got {self.decode_attention_backend_str}"
)
else:
if use_mla_backend: # !FA4 + MLA
KV4_ATTENTION_MLA_BACKEND_CHOICES = [
"cutlass_mla",
"flashinfer",
"trtllm_mla",
"flashmla",
]
assert (
self.attention_backend in KV4_ATTENTION_MLA_BACKEND_CHOICES
), (
f"KV4 MLA expects attention_backend to be one of "
f"{KV4_ATTENTION_MLA_BACKEND_CHOICES}, but got {self.attention_backend}"
)
else: # !FA4 + MHA
KV4_ATTENTION_MHA_BACKEND_CHOICES = [
"triton",
"torch_native",
"flex_attention",
"trtllm_mha",
]
assert (
self.attention_backend in KV4_ATTENTION_MHA_BACKEND_CHOICES
), (
f"KV4 MHA expects attention_backend to be one of "
f"{KV4_ATTENTION_MHA_BACKEND_CHOICES}, but got {self.attention_backend}"
)
else:
raise RuntimeError("KV4 is not tested on non-CUDA platforms.")
def _handle_page_size(self):
if self.page_size is None:
if not is_musa():
self.page_size = 1
else:
self.page_size = 64
def _handle_amd_specifics(self):
if is_hip():
self.triton_attention_num_kv_splits = 16
def _handle_nccl_pre_warm(self):
# pre_warm_nccl is only used with CUDA or HIP hardware
if self.pre_warm_nccl and not (is_cuda() or is_hip()):
logger.warning(
"pre_warm_nccl is only applicable for CUDA or HIP hardware. "
"Ignoring pre_warm_nccl setting on current hardware."
)
self.pre_warm_nccl = False
def _handle_grammar_backend(self):
if self.grammar_backend is None:
self.grammar_backend = "xgrammar"
def _handle_mamba_backend(self):
if self.mamba_backend == "flashinfer":
if is_flashinfer_available():
try:
import flashinfer.mamba # noqa: F401
logger.info("Successfully imported FlashInfer mamba module")
except (ImportError, AttributeError):
raise ValueError(
"FlashInfer mamba module not available, please check flashinfer installation."
)
else:
raise ValueError(
"FlashInfer mamba module not available, please check flashinfer installation."
)
def _handle_linear_attn_backend(self):
import torch
# SM100+: default to FlashInfer GDN decode when the user hasn't
# explicitly chosen a decode backend and mamba-ssm-dtype is bf16
# (required by FlashInfer GDN on SM100+).
# Fixed in FlashInfer v0.6.7: flashinfer-ai/flashinfer#2810
# Excluded when MTP speculative decoding is enabled because
# FlashInfer GDN MTP verify is not yet supported on SM100+.
if (
self.linear_attn_decode_backend is None
and is_sm100_supported()
and self.mamba_ssm_dtype == "bfloat16"
and self.speculative_algorithm is None
):
self.linear_attn_decode_backend = "flashinfer"
logger.info(
"SM100+ detected with mamba-ssm-dtype=bfloat16, "
"defaulting --linear-attn-decode-backend to flashinfer."
)
# SM100+ FlashInfer GDN decode requires bf16 state; SM90 uses float32.
decode = self.linear_attn_decode_backend or self.linear_attn_backend
if (
decode == "flashinfer"
and self.mamba_ssm_dtype != "bfloat16"
and torch.cuda.is_available()
and torch.cuda.get_device_capability()[0] >= 10
):
raise ValueError(
"--linear-attn-decode-backend flashinfer on SM100+ requires "
"--mamba-ssm-dtype bfloat16, "
f"got {self.mamba_ssm_dtype!r}"
)
def _handle_context_parallelism(self):
if self.attn_cp_size > 1:
# The tp_size is the world size, not the real tensor parallel size
assert (
self.tp_size % self.attn_cp_size == 0
), "tp_size must be divisible by attn_cp_size"
assert (
self.tp_size % (self.dp_size * self.attn_cp_size) == 0
), "tp_size must be divisible by dp_size * attn_cp_size"
assert (
not self.enable_aiter_allreduce_fusion
), "Aiter allreduce fusion is not supported with context parallelism"
if self.moe_dp_size > 1:
# The tp_size is the world size, not the real tensor parallel size
assert (
self.tp_size % self.moe_dp_size == 0
), "tp_size must be divisible by moe_dp_size"
assert (
self.ep_size * self.moe_dp_size <= self.tp_size
), "ep_size * moe_dp_size must be less than or equal to tp_size"
assert self.pp_size == 1, "PP is not supported with context parallelism"
if self.ep_size > 1:
assert (
self.ep_size * self.moe_dp_size == self.tp_size
), "ep_size * moe_dp_size must be equal to tp_size"
assert (
not self.enable_aiter_allreduce_fusion
), "Aiter allreduce fusion is not supported with context parallelism"
if self.attn_cp_size != self.moe_dp_size:
assert (
self.moe_dp_size == 1
), "attn_cp_size != moe_dp_size is only supported when moe_dp_size == 1"
def _handle_data_parallelism(self):
if self.dp_size == 1:
self.enable_dp_attention = False
self.enable_dp_lm_head = False
if self.enable_dp_attention:
self.schedule_conservativeness = self.schedule_conservativeness * 0.3
assert self.tp_size % self.dp_size == 0
self.chunked_prefill_size = self.chunked_prefill_size // self.dp_size
logger.warning(
f"DP attention is enabled. The chunked prefill size is adjusted to {self.chunked_prefill_size} to avoid MoE kernel issues. "
)
if self.enable_dp_lm_head:
assert (
self.enable_dp_attention
), "Please enable dp attention when setting enable_dp_lm_head. "
def _handle_moe_kernel_config(self):
if self.quantization == "mxfp8":
if self.moe_runner_backend == "auto":
self.moe_runner_backend = "flashinfer_trtllm"
elif self.moe_runner_backend not in [
"cutlass",
"flashinfer_trtllm",
"flashinfer_trtllm_routed",
]:
logger.warning(
"mxfp8 quantization supports only cutlass, flashinfer_trtllm, "
"or flashinfer_trtllm_routed backends. "
f"Overriding {self.moe_runner_backend!r}."
)
self.moe_runner_backend = "flashinfer_trtllm"
if self.moe_runner_backend == "flashinfer_cutlass":
assert self.quantization in [
"modelopt_fp4",
"modelopt_fp8",
"modelopt_mixed",
None,
], f"Invalid quantization '{self.quantization}'. \nFlashInfer Cutlass MOE supports only: 'modelopt_fp4', 'modelopt_fp8', 'modelopt_mixed', or bfloat16 (None)."
assert self.ep_size in [
1,
self.tp_size,
], "The expert parallel size must be 1 or the same as the tensor parallel size"
if self.moe_runner_backend == "flashinfer_cutedsl":
assert self.quantization in [
"modelopt_fp4"
], f"Invalid quantization '{self.quantization}'. \nFlashInfer CuteDSL MOE currently supports only: 'modelopt_fp4'."
assert self.ep_size in [
1,
self.tp_size,
], "The expert parallel size must be 1 or the same as the tensor parallel size"
assert self.moe_a2a_backend in [
"none",
"deepep",
], (
f"flashinfer_cutedsl supports moe_a2a_backend='none' (standard path) "
f"or 'deepep' (DeepEP low-latency path), got '{self.moe_a2a_backend}'."
)
self.disable_shared_experts_fusion = True
logger.warning(
"FlashInfer CuteDSL MoE is enabled. --disable-shared-experts-fusion is automatically set."
)
if self.moe_runner_backend == "flashinfer_trtllm":
assert self.quantization in [
"modelopt_fp4",
"fp8",
"mxfp8",
"modelopt_fp8",
"modelopt_mixed",
"compressed-tensors",
None,
], f"Invalid quantization '{self.quantization}'. \nFlashInfer TRTLLM MOE supports only: 'modelopt_fp4', 'fp8', 'modelopt_fp8', 'modelopt_mixed', 'compressed-tensors', or bfloat16 (None)."
self.disable_shared_experts_fusion = True
logger.warning(
"FlashInfer TRTLLM MoE is enabled. --disable-shared-experts-fusion is automatically set."
)
if self.moe_runner_backend == "flashinfer_trtllm_routed":
assert self.quantization in [
"fp8",
"mxfp8",
"modelopt_fp4",
None,
], f"Invalid quantization '{self.quantization}'. \nFlashInfer TRTLLM routed MOE supports only: 'fp8', 'mxfp8', 'modelopt_fp4', or bfloat16 (None)."
self.disable_shared_experts_fusion = True
logger.warning(
"FlashInfer TRTLLM routed MoE is enabled. --disable-shared-experts-fusion is automatically set."
)
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"
)
assert self.quantization in [
"fp8",
"mxfp8",
], "cutlass MoE is only supported with fp8/mxfp8 quantization"
self.moe_runner_backend = "cutlass"
if self.moe_runner_backend == "cutlass" and self.quantization in [
"fp8",
"mxfp8",
]:
assert (
self.ep_size == 1
), "FP8/MXFP8 Cutlass MoE is only supported with ep_size == 1"
# TODO(yuwei): Fix piecewise cuda graph support for bypassed topk MoE backends.
# Exception: GptOssForCausalLM wraps the entire MoE block in its own
# custom op (moe_impl), so bypassed topk is handled inside the op body.
if (
not self.enforce_piecewise_cuda_graph
and self.moe_runner_backend in ("flashinfer_trtllm", "flashinfer_mxfp4")
and self.get_model_config().hf_config.architectures[0]
!= "GptOssForCausalLM"
):
self.disable_piecewise_cuda_graph = True
logger.info(
f"Piecewise cuda graph is disabled for MoE runner backend "
f"'{self.moe_runner_backend}' (bypassed topk is incompatible "
f"with torch.compile)."
)
def _handle_a2a_moe(self):
if self.moe_a2a_backend == "deepep":
if self.deepep_mode == "normal":
logger.warning("Cuda graph is disabled because deepep_mode=`normal`")
self.disable_cuda_graph = True
self.ep_size = self.tp_size
logger.warning(
f"DeepEP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
if self.moe_a2a_backend == "mooncake":
self.ep_size = self.tp_size
logger.warning(
f"Mooncake MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
if self.moe_a2a_backend == "nixl":
self.ep_size = self.tp_size
logger.warning(
f"Nixl MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
if self.moe_a2a_backend == "ascend_fuseep":
self.ep_size = self.tp_size
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 = envs.SGLANG_NPU_FUSED_MOE_MODE.get()
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:
assert (
self.quantization == "modelslim"
), "When fuse_mode is set to 2, the NPU supports only ModelSlim quantization."
if self.moe_a2a_backend == "flashinfer":
self.ep_size = self.tp_size
logger.warning(
f"Flashinfer MoE A2A is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
self.disable_shared_experts_fusion = True
logger.warning(
"Flashinfer MoE A2A is enabled. --disable-shared-experts-fusion is automatically set."
)
if self.deepep_mode != "auto":
logger.warning("--deepep-mode is ignored for Flashinfer MoE A2A")
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"
)
assert self.moe_runner_backend in [
"flashinfer_cutlass"
], "Flashinfer MoE A2A is only supported with flashinfer_cutlass moe runner backend"
if self.moe_a2a_backend == "mori":
self.ep_size = self.tp_size
if self.deepep_mode == "auto":
self.deepep_mode = "normal"
logger.warning("auto set deepep_mode=`normal` for MORI EP")
logger.warning(
f"MoRI MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
# Check chunked prefill for mori
# 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
) <= 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):
self.expert_distribution_recorder_mode = "stat"
logger.warning(
"EPLB is enabled. The expert_distribution_recorder_mode is automatically set."
)
if (self.enable_eplb or (self.init_expert_location != "trivial")) and (
self.ep_dispatch_algorithm is None
):
self.ep_dispatch_algorithm = "static"
if self.enable_eplb:
assert self.ep_size > 1
def _handle_elastic_ep(self):
if self.elastic_ep_backend is not None:
if self.enable_eplb:
if self.eplb_algorithm == "auto":
self.eplb_algorithm = "elasticity_aware"
assert self.eplb_algorithm in [
"elasticity_aware",
"elasticity_aware_hierarchical",
], "Elastic EP requires eplb_algorithm to be set to 'auto' or 'elasticity_aware(_hierarchical)'."
assert self.pp_size == 1, "PP size should be set to 1 under elastic EP"
if self.elastic_ep_backend == "mooncake":
self.mooncake_ib_device = self._validate_ib_devices(
self.mooncake_ib_device
)
if self.elastic_ep_rejoin:
assert (
self.elastic_ep_backend is not None
), "Elastic EP rejoin requires elastic_ep_backend to be set."
def _handle_expert_distribution_metrics(self):
if self.enable_expert_distribution_metrics and (
self.expert_distribution_recorder_mode is None
):
self.expert_distribution_recorder_mode = "stat"
if self.expert_distribution_recorder_buffer_size is None:
if (x := self.eplb_rebalance_num_iterations) is not None:
self.expert_distribution_recorder_buffer_size = x
elif self.expert_distribution_recorder_mode is not None:
self.expert_distribution_recorder_buffer_size = 1000
def _handle_pipeline_parallelism(self):
if self.pp_size > 1:
self.disable_overlap_schedule = True
logger.warning(
"Pipeline parallelism is incompatible with overlap schedule."
)
def _handle_hicache(self):
"""Normalize hicache-related knobs into a valid runtime configuration.
Resolution order:
1) Layout <-> I/O compatibility for direct conflicts.
2) Storage <-> layout compatibility (may rewrite layout).
3) I/O <-> decode-attention compatibility (may rewrite I/O or decode backend).
4) Re-run step (1) if step (3) changed I/O backend.
"""
# Skip all normalization when neither hicache nor decode-offload path is active.
if not (
self.enable_hierarchical_cache
or self.disaggregation_decode_enable_offload_kvcache
):
return
# Step 1: Initial layout-io compatibility normalization.
self._resolve_layout_io_compatibility()
# Step 2: Storage-layout normalization without changing io backend.
self._resolve_storage_layout_compatibility()
# Step 3: IO-decode backend compatibility (may change io backend).
io_changed = self._resolve_io_decode_attention_compatibility()
# Step 4: Re-normalize layout after io backend changes.
if io_changed:
self._resolve_layout_io_compatibility()
def _resolve_layout_io_compatibility(self):
if (
self.hicache_mem_layout == "page_first_direct"
and self.hicache_io_backend == "kernel"
):
self.hicache_io_backend = "direct"
logger.warning(
"Kernel io backend does not support page first direct layout, switching to direct io backend"
)
if (
self.hicache_mem_layout == "page_first"
and self.hicache_io_backend == "direct"
):
self.hicache_mem_layout = "page_first_direct"
logger.warning(
"Page first layout is not supported with direct IO backend, switching to page first direct layout"
)
def _resolve_storage_layout_compatibility(self):
if (
self.hicache_storage_backend != "mooncake"
or self.hicache_mem_layout != "layer_first"
):
return
if self.hicache_io_backend == "direct":
new_layout = "page_first_direct"
elif self.hicache_io_backend == "kernel":
new_layout = "page_first"
else:
# Keep current behavior for unknown backends (e.g., kernel_ascend).
new_layout = self.hicache_mem_layout
self.hicache_mem_layout = new_layout
logger.warning(
f"Mooncake storage backend does not support layer_first layout, "
f"switching to {new_layout} layout for {self.hicache_io_backend} io backend"
)
def _resolve_io_decode_attention_compatibility(self) -> bool:
if self.hicache_io_backend != "kernel":
return False
# Only patch settings when the effective decode backend is FA3.
effective_decode_backend = (
self.decode_attention_backend or self.attention_backend
)
if effective_decode_backend != "fa3":
return False
if self.decode_attention_backend is not None:
self.hicache_io_backend = "direct"
logger.warning(
"FlashAttention3 decode backend is not compatible with hierarchical cache. "
"Setting hicache_io_backend to vanilla I/O, which may lead to suboptimal performance with small page sizes."
)
return True
# If decode backend is implicit, pick a safe backend without changing io backend.
if not self.use_mla_backend():
# FlashInfer does not support attention sinks.
if (
is_flashinfer_available()
and not self.get_model_config().has_attention_sinks
):
self.decode_attention_backend = "flashinfer"
else:
self.decode_attention_backend = "triton"
else:
self.decode_attention_backend = (
"flashinfer" if is_sm100_supported() else "triton"
)
return False
def _handle_speculative_decoding(self):
if (
self.speculative_draft_model_path is not None
and self.speculative_draft_model_revision is None
):
self.speculative_draft_model_revision = "main"
# FlashInfer trtllm moe bf16 only support RenormalizeNaive routing method and Deepseek routing method
# It is hard to tell the routing method in draft model, and the moe layer in draft model is not the bottleneck among
# end to end, so we just avoid using trtllm_moe for speculative decoding.
from sglang.srt.layers.moe.utils import MoeRunnerBackend
if self.speculative_moe_runner_backend is None:
self.speculative_moe_runner_backend = (
"auto"
if self.moe_runner_backend
in ["flashinfer_trtllm", "flashinfer_trtllm_routed"]
else self.moe_runner_backend
)
else:
assert not MoeRunnerBackend(
self.speculative_moe_runner_backend
).is_flashinfer_trtllm(), "Currently speculative MoE runner backend doesn't support flashinfer_trtllm, please use triton or auto backend for speculative moe runner instead."
if self.speculative_algorithm == "NEXTN":
self.speculative_algorithm = "EAGLE"
if self.speculative_algorithm == "DFLASH":
if self.enable_dp_attention:
raise ValueError(
"Currently DFLASH speculative decoding does not support dp attention."
)
if self.pp_size != 1:
raise ValueError(
"Currently DFLASH speculative decoding only supports pp_size == 1."
)
if self.speculative_draft_model_path is None:
raise ValueError(
"DFLASH speculative decoding requires setting --speculative-draft-model-path."
)
# DFLASH does not use EAGLE-style `num_steps`/`topk`, but those fields still
# affect generic scheduler/KV-cache accounting (buffer sizing, KV freeing,
# RoPE reservation). Force them to 1 to avoid surprising memory behavior.
#
# For DFlash, the natural unit is `block_size` (verify window length).
if self.speculative_num_steps is None:
self.speculative_num_steps = 1
elif int(self.speculative_num_steps) != 1:
logger.warning(
"DFLASH only supports speculative_num_steps == 1; overriding speculative_num_steps=%s to 1.",
self.speculative_num_steps,
)
self.speculative_num_steps = 1
if self.speculative_eagle_topk is None:
self.speculative_eagle_topk = 1
elif int(self.speculative_eagle_topk) != 1:
logger.warning(
"DFLASH only supports speculative_eagle_topk == 1; overriding speculative_eagle_topk=%s to 1.",
self.speculative_eagle_topk,
)
self.speculative_eagle_topk = 1
if self.speculative_dflash_block_size is not None:
if int(self.speculative_dflash_block_size) <= 0:
raise ValueError(
"DFLASH requires --speculative-dflash-block-size to be positive, "
f"got {self.speculative_dflash_block_size}."
)
if self.speculative_num_draft_tokens is not None and int(
self.speculative_num_draft_tokens
) != int(self.speculative_dflash_block_size):
raise ValueError(
"Both --speculative-num-draft-tokens and --speculative-dflash-block-size are set "
"but they differ. For DFLASH they must match. "
f"speculative_num_draft_tokens={self.speculative_num_draft_tokens}, "
f"speculative_dflash_block_size={self.speculative_dflash_block_size}."
)
self.speculative_num_draft_tokens = int(
self.speculative_dflash_block_size
)
window_size = None
if self.speculative_dflash_draft_window_size is not None:
window_size = int(self.speculative_dflash_draft_window_size)
if window_size <= 0:
raise ValueError(
"DFLASH requires --speculative-dflash-draft-window-size "
f"to be positive, got {window_size}."
)
self.speculative_dflash_draft_window_size = window_size
if self.speculative_num_draft_tokens is None:
from sglang.srt.speculative.dflash_utils import (
parse_dflash_draft_config,
)
model_override_args = json.loads(self.json_model_override_args)
inferred_block_size = None
try:
from sglang.srt.utils.hf_transformers_utils import get_config
draft_hf_config = get_config(
self.speculative_draft_model_path,
trust_remote_code=self.trust_remote_code,
revision=self.speculative_draft_model_revision,
model_override_args=model_override_args,
)
inferred_block_size = parse_dflash_draft_config(
draft_hf_config=draft_hf_config
).resolve_block_size(default=None)
except Exception as e:
logger.warning(
"Failed to infer DFLASH block_size from draft model config; "
"defaulting speculative_num_draft_tokens to 16. Error: %s",
e,
)
if inferred_block_size is None:
inferred_block_size = 16
logger.warning(
"speculative_num_draft_tokens is not set; defaulting to %d for DFLASH.",
inferred_block_size,
)
self.speculative_num_draft_tokens = inferred_block_size
if window_size is not None:
draft_tokens = int(self.speculative_num_draft_tokens)
if window_size < draft_tokens:
raise ValueError(
"DFLASH --speculative-dflash-draft-window-size must be >= "
"--speculative-num-draft-tokens (block_size). "
f"window_size={window_size}, block_size={draft_tokens}."
)
if self.max_running_requests is None:
self.max_running_requests = 48
logger.warning(
"Max running requests is reset to 48 for speculative decoding. You can override this by explicitly setting --max-running-requests."
)
self.disable_overlap_schedule = True
logger.warning(
"Overlap scheduler is disabled when using DFLASH speculative decoding (spec v2 is not supported yet)."
)
if self.enable_mixed_chunk:
self.enable_mixed_chunk = False
logger.warning(
"Mixed chunked prefill is disabled because of using dflash speculative decoding."
)
if self.speculative_algorithm in ("EAGLE", "EAGLE3", "STANDALONE"):
if self.speculative_algorithm == "STANDALONE" and self.enable_dp_attention:
# TODO: support dp attention for standalone speculative decoding
raise ValueError(
"Currently standalone speculative decoding does not support dp attention."
)
if self.max_running_requests is None:
self.max_running_requests = 48
logger.warning(
"Max running requests is reset to 48 for speculative decoding. You can override this by explicitly setting --max-running-requests."
)
if (
self.speculative_algorithm in ["EAGLE", "EAGLE3", "STANDALONE"]
and envs.SGLANG_ENABLE_SPEC_V2.get()
):
self.disable_overlap_schedule = False
logger.warning(
"Spec v2 is enabled for eagle/eagle3 speculative decoding and overlap schedule is turned on."
)
if (
self.speculative_eagle_topk is not None
and self.speculative_eagle_topk > 1
):
raise ValueError(
"Spec v2 currently only supports topk = 1 for speculative decoding."
)
else:
self.disable_overlap_schedule = True
logger.warning(
"Overlap scheduler is disabled when spec v2 is off or using unsupported speculative algorithm. "
"You can set env SGLANG_ENABLE_SPEC_V2=True to enable the experimental overlap scheduler. "
)
if self.enable_mixed_chunk:
self.enable_mixed_chunk = False
logger.warning(
"Mixed chunked prefill is disabled because of using "
"eagle speculative decoding."
)
model_arch = self.get_model_config().hf_config.architectures[0]
if model_arch in [
"DeepseekV32ForCausalLM",
"DeepseekV3ForCausalLM",
"Glm4MoeForCausalLM",
"Glm4MoeLiteForCausalLM",
"GlmMoeDsaForCausalLM",
"BailingMoeForCausalLM",
"BailingMoeV2ForCausalLM",
"BailingMoeV2_5ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
"HYV3ForCausalLM",
]:
if self.speculative_draft_model_path is None:
self.speculative_draft_model_path = self.model_path
self.speculative_draft_model_revision = self.revision
else:
if model_arch not in [
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
]:
logger.warning(
"DeepSeek MTP does not require setting speculative_draft_model_path."
)
if self.speculative_num_steps is None:
assert (
self.speculative_eagle_topk is None
and self.speculative_num_draft_tokens is None
)
(
self.speculative_num_steps,
self.speculative_eagle_topk,
self.speculative_num_draft_tokens,
) = auto_choose_speculative_params(self)
if (
self.attention_backend == "trtllm_mha"
or self.decode_attention_backend == "trtllm_mha"
or self.prefill_attention_backend == "trtllm_mha"
):
if self.speculative_eagle_topk > 1:
raise ValueError(
"trtllm_mha backend only supports topk = 1 for speculative decoding."
)
if (
self.speculative_eagle_topk == 1
and self.speculative_num_draft_tokens != self.speculative_num_steps + 1
):
logger.warning(
"speculative_num_draft_tokens is adjusted to speculative_num_steps + 1 when speculative_eagle_topk == 1"
)
self.speculative_num_draft_tokens = self.speculative_num_steps + 1
if (
self.speculative_eagle_topk > 1
and self.page_size > 1
and self.attention_backend not in ["flashinfer", "fa3"]
):
raise ValueError(
"speculative_eagle_topk > 1 with page_size > 1 is unstable and produces incorrect results for paged attention backends. This combination is only supported for the 'flashinfer' backend."
)
if self.speculative_algorithm == "NGRAM":
if not self.device.startswith("cuda"):
raise ValueError(
"Ngram speculative decoding only supports CUDA device."
)
if self.max_running_requests is None:
self.max_running_requests = 48
logger.warning(
"Max running requests is reset to 48 for speculative decoding. You can override this by explicitly setting --max-running-requests."
)
self.disable_overlap_schedule = True
self.enable_mixed_chunk = False
self.speculative_eagle_topk = self.speculative_ngram_max_bfs_breadth
if self.speculative_num_draft_tokens is None:
self.speculative_num_draft_tokens = 12
logger.warning(
"speculative_num_draft_tokens is set to 12 by default for ngram speculative decoding. "
"You can override this by explicitly setting --speculative-num-draft-tokens."
)
if self.speculative_ngram_external_corpus_path is not None:
if self.speculative_ngram_external_sam_budget <= 0:
raise ValueError(
"--speculative-ngram-external-sam-budget must be positive when "
"--speculative-ngram-external-corpus-path is set."
)
if self.speculative_ngram_external_corpus_max_tokens <= 0:
raise ValueError(
"--speculative-ngram-external-corpus-max-tokens must be positive when "
"--speculative-ngram-external-corpus-path is set."
)
if (
self.speculative_ngram_external_sam_budget
> self.speculative_num_draft_tokens - 1
):
raise ValueError(
"speculative_ngram_external_sam_budget must be less than or equal to "
f"speculative_num_draft_tokens - 1 ({self.speculative_num_draft_tokens - 1})."
)
logger.warning(
"The overlap scheduler and mixed chunked prefill are disabled because of "
"using ngram speculative decoding."
)
if (
self.speculative_eagle_topk > 1
and self.page_size > 1
and self.attention_backend != "flashinfer"
):
raise ValueError(
f"speculative_eagle_topk({self.speculative_eagle_topk}) > 1 "
f"with page_size({self.page_size}) > 1 is unstable "
"and produces incorrect results for paged attention backends. "
"This combination is only supported for the 'flashinfer' backend."
)
if self.enable_dp_attention:
# TODO: support dp attention for ngram speculative decoding
raise ValueError(
"Currently ngram speculative decoding does not support dp attention."
)
if self.speculative_adaptive:
from sglang.srt.speculative.adaptive_spec_params import (
adaptive_unsupported_reason,
)
reason = adaptive_unsupported_reason(self)
if reason is not None:
logger.warning(
f"speculative_adaptive disabled: {reason}. "
"Falling back to static speculative params."
)
self.speculative_adaptive = False
def _handle_load_format(self):
if (
self.load_format == "auto" or self.load_format == "gguf"
) and check_gguf_file(self.model_path):
self.quantization = self.load_format = "gguf"
if self.load_format == "auto" and self._is_mistral_native_format():
self.load_format = "mistral"
logger.info(
"Detected Mistral native format checkpoint, setting load_format='mistral'"
)
if is_runai_obj_uri(self.model_path):
self.load_format = "runai_streamer"
elif is_remote_url(self.model_path):
self.load_format = "remote"
if self.custom_weight_loader is None:
self.custom_weight_loader = []
if self.load_format == "remote_instance":
if self.remote_instance_weight_loader_backend == "modelexpress":
# ModelExpress backend: requires url in --modelexpress-config
if self.modelexpress_url is None:
logger.warning(
"Fallback load_format to 'auto' due to missing 'url' in --modelexpress-config."
)
self.load_format = "auto"
elif not self.validate_transfer_engine():
logger.warning(
"Fallback load_format to 'auto' due to 'transfer_engine' (required by modelexpress) not being supported."
)
self.load_format = "auto"
elif (
self.remote_instance_weight_loader_seed_instance_ip is None
or self.remote_instance_weight_loader_seed_instance_service_port is None
):
logger.warning(
"Fallback load_format to 'auto' due to incomplete remote instance weight loader settings."
)
self.load_format = "auto"
elif (
self.remote_instance_weight_loader_send_weights_group_ports is None
and self.remote_instance_weight_loader_backend == "nccl"
):
logger.warning(
"Fallback load_format to 'auto' due to incomplete remote instance weight loader NCCL group ports settings."
)
self.load_format = "auto"
elif (
self.remote_instance_weight_loader_backend == "transfer_engine"
and not self.validate_transfer_engine()
):
logger.warning(
"Fallback load_format to 'auto' due to 'transfer_engine' backend is not supported."
)
self.load_format = "auto"
# Check whether TransferEngine can be used when users want to start seed service that supports TransferEngine backend.
if self.remote_instance_weight_loader_start_seed_via_transfer_engine:
self.remote_instance_weight_loader_start_seed_via_transfer_engine = (
self.validate_transfer_engine()
)
def _is_mistral_native_format(self) -> bool:
"""Detect if the model uses Mistral native format (params.json + consolidated weights).
When both params.json and config.json exist, default to HF format to
avoid weight-name mismatches (e.g. Mistral-7B-Instruct-v0.3).
Exception: models routed through ``_load_mistral_large_3_for_causal_LM``
(mistral-large-3, mistral-small-4, leanstral) build their config from
params.json and expect native weight names, so native format is required
even when config.json is also present.
"""
# Keep in sync with the name checks in
# hf_transformers_utils.py::get_config / get_tokenizer.
_MISTRAL_NATIVE_CONFIG_PATTERNS = (
"mistral-large-3",
"mistral-small-4",
"leanstral",
)
def _check_format(has_params: bool, has_hf_config: bool) -> bool:
if has_params and not has_hf_config:
return True
if has_params and has_hf_config:
model_lower = str(self.model_path).lower()
if any(name in model_lower for name in _MISTRAL_NATIVE_CONFIG_PATTERNS):
return True
return False
if os.path.isdir(self.model_path):
has_params = os.path.exists(os.path.join(self.model_path, "params.json"))
has_hf_config = os.path.exists(os.path.join(self.model_path, "config.json"))
return _check_format(has_params, has_hf_config)
# For hub models, check remote files
try:
from huggingface_hub import HfApi
files = {s.rfilename for s in HfApi().model_info(self.model_path).siblings}
return _check_format("params.json" in files, "config.json" in files)
except Exception:
return False
def _handle_pd_disaggregation(self):
if self.disaggregation_mode == "decode":
self.disable_radix_cache = True
logger.warning("KV cache is forced as chunk cache for decode server")
elif self.disaggregation_mode == "prefill":
assert (
self.disaggregation_transfer_backend != "fake"
), "Prefill server does not support 'fake' as the transfer backend"
if self.disable_piecewise_cuda_graph:
self.disable_cuda_graph = True
logger.warning(
"Cuda graph is disabled for prefill server when piecewise cuda graph is not enabled."
)
if self.disaggregation_mode in ("prefill", "decode"):
if (
envs.SGLANG_DISAGG_STAGING_BUFFER.get()
and self.disaggregation_transfer_backend != "mooncake"
):
raise ValueError(
f"SGLANG_DISAGG_STAGING_BUFFER requires "
f"disaggregation_transfer_backend='mooncake', "
f"got '{self.disaggregation_transfer_backend}'."
)
def _handle_encoder_disaggregation(self):
if self.enable_prefix_mm_cache and not self.encoder_only:
raise ValueError(
"--enable-prefix-mm-cache requires --encoder-only to be enabled"
)
if self.encoder_only and self.language_only:
raise ValueError("Cannot set --encoder-only and --language-only together")
if self.encoder_only and not self.disaggregation_mode == "null":
raise ValueError(
"Cannot set --encoder-only and --disaggregation-mode prefill/decode together"
)
if self.language_only and len(self.encoder_urls) == 0:
raise ValueError(
"requires at least one encoder urls to be set via --encoder-urls"
)
# Validate IB devices when mooncake backend is used
if (
self.disaggregation_transfer_backend == "mooncake"
and self.disaggregation_mode in ("prefill", "decode")
) or self.encoder_transfer_backend == "mooncake":
self.disaggregation_ib_device = self._validate_ib_devices(
self.disaggregation_ib_device
)
# Validate model type: only support Qwen models for now
hf_config = self.get_model_config().hf_config
model_arch = hf_config.architectures[0]
if (self.encoder_only or self.language_only) and model_arch not in [
"Qwen2VLForConditionalGeneration",
"Qwen3VLForConditionalGeneration",
"Qwen2_5_VLForConditionalGeneration",
"Qwen3VLMoeForConditionalGeneration",
"Qwen3_5ForConditionalGeneration",
"Qwen3_5MoeForConditionalGeneration",
"Qwen3OmniMoeForConditionalGeneration",
"Qwen2AudioForConditionalGeneration",
"Qwen2_5OmniForConditionalGeneration",
"KimiVLForConditionalGeneration",
"KimiK25ForConditionalGeneration",
]:
raise ValueError(
f"Model type {model_arch} is not supported for encoder disaggregation, only Qwen models are supported for now."
)
def _validate_ib_devices(self, device_str: str) -> Optional[str]:
"""
Validate IB devices before passing to mooncake.
Args:
device_str: Comma-separated IB device names (e.g., "mlx5_0,mlx5_1")
Returns:
Normalized comma-separated string of validated device names, or None if input is None.
"""
if device_str is None:
logger.warning(
"No IB devices specified for Mooncake backend, falling back to auto discovery."
)
return None
# Strip whitespace from device names
devices = [d.strip() for d in device_str.split(",") if d.strip()]
if len(devices) == 0:
raise ValueError("No valid IB devices specified")
# Deduplicate while preserving order
unique_devices = list(dict.fromkeys(devices))
if len(unique_devices) != len(devices):
logger.warning(
"Duplicate IB devices specified: %s. Deduplicating to: %s",
device_str,
",".join(unique_devices),
)
devices = unique_devices
# Get available IB devices from sysfs
ib_sysfs_path = "/sys/class/infiniband"
if not os.path.isdir(ib_sysfs_path):
raise RuntimeError(
f"InfiniBand sysfs path not found: {ib_sysfs_path}. "
"Please ensure InfiniBand drivers are installed."
)
available_devices = set(os.listdir(ib_sysfs_path))
if len(available_devices) == 0:
raise RuntimeError(f"No IB devices found in {ib_sysfs_path}")
# Check for invalid devices
invalid_devices = [d for d in devices if d not in available_devices]
if len(invalid_devices) != 0:
raise ValueError(
f"Invalid IB devices specified: {invalid_devices}. "
f"Available devices: {sorted(available_devices)}"
)
return ",".join(devices)
def _handle_tokenizer_batching(self):
if self.enable_tokenizer_batch_encode and self.enable_dynamic_batch_tokenizer:
raise ValueError(
"Cannot enable both --enable-tokenizer-batch-encode and --enable-dynamic-batch-tokenizer. "
"Please choose one tokenizer batching approach."
)
if self.skip_tokenizer_init:
if self.tokenizer_worker_num != 1:
logger.warning(
"skip_tokenizer_init=True disables tokenizer workers; forcing tokenizer_worker_num=1 "
f"(requested {self.tokenizer_worker_num})."
)
self.tokenizer_worker_num = 1
if self.enable_tokenizer_batch_encode:
logger.warning(
"skip_tokenizer_init=True ignores --enable-tokenizer-batch-encode; disabling it."
)
self.enable_tokenizer_batch_encode = False
if self.enable_dynamic_batch_tokenizer:
logger.warning(
"skip_tokenizer_init=True ignores --enable-dynamic-batch-tokenizer; disabling it."
)
self.enable_dynamic_batch_tokenizer = False
def _handle_environment_variables(self):
envs.SGLANG_ENABLE_TORCH_COMPILE.set("1" if self.enable_torch_compile else "0")
if self.mamba_ssm_dtype is not None:
envs.SGLANG_MAMBA_SSM_DTYPE.set(self.mamba_ssm_dtype)
envs.SGLANG_DISABLE_OUTLINES_DISK_CACHE.set(
"1" if self.disable_outlines_disk_cache else "0"
)
envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.set(
"1" if self.enable_deterministic_inference else "0"
)
if self.debug_cuda_graph:
if not is_cuda():
logger.warning(
"--debug-cuda-graph is not supported on non CUDA devices. "
"Disabling breakable CUDA graph."
)
self.debug_cuda_graph = False
else:
envs.SGLANG_USE_BREAKABLE_CUDA_GRAPH.set("1")
logger.warning(
"Debug mode for CUDA graph is enabled via breakable CUDA graph. "
"All operations will run eagerly through the graph capture/replay path."
)
def _handle_cache_compatibility(self):
if self.enable_hierarchical_cache and self.disable_radix_cache:
raise ValueError(
"The arguments enable-hierarchical-cache and disable-radix-cache are mutually exclusive "
"and cannot be used at the same time. Please use only one of them."
)
if self.disaggregation_decode_enable_offload_kvcache:
if self.disaggregation_mode != "decode":
raise ValueError(
"The argument disaggregation-decode-enable-offload-kvcache is only supported for decode side."
)
if self.hicache_storage_backend is None:
raise ValueError(
"The argument disaggregation-decode-enable-offload-kvcache is only supported when hicache-storage-backend is provided."
)
if not (0 < self.swa_full_tokens_ratio <= 1.0):
raise ValueError("--swa-full-tokens-ratio should be in range (0, 1.0].")
def _handle_deterministic_inference(self):
if self.rl_on_policy_target is not None:
logger.warning(
"Enable deterministic inference because of rl_on_policy_target."
)
self.enable_deterministic_inference = True
# For VLM
envs.SGLANG_VLM_CACHE_SIZE_MB.set(0)
# TODO remove this environment variable as a whole
envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.set(True)
if self.enable_deterministic_inference:
if self.enable_aiter_allreduce_fusion:
logger.warning(
"Disable --enable-aiter-allreduce-fusion because deterministic inference is enabled."
)
self.enable_aiter_allreduce_fusion = False
# Check sampling backend
self.sampling_backend = "pytorch"
logger.warning(
"Sampling backend is set to pytorch for deterministic inference."
)
is_deepseek_model = False
if parse_connector_type(self.model_path) != ConnectorType.INSTANCE:
try:
hf_config = self.get_model_config().hf_config
model_arch = hf_config.architectures[0]
is_deepseek_model = model_arch in [
"DeepseekV2ForCausalLM",
"DeepseekV3ForCausalLM",
"DeepseekV32ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
"GlmMoeDsaForCausalLM",
]
except Exception:
pass
# Check attention backend
if self.attention_backend is None:
# User didn't specify attention backend, fallback based on GPU architecture
if is_sm100_supported() or is_sm120_supported():
# Blackwell and newer architectures
if is_deepseek_model:
# fallback to triton for DeepSeek models because flashinfer doesn't support deterministic inference for DeepSeek models yet
self.attention_backend = "triton"
else:
# fallback to flashinfer on Blackwell for non-DeepSeek models
self.attention_backend = "flashinfer"
else:
# Hopper (SM90) and older architectures
self.attention_backend = "fa3"
logger.warning(
f"Attention backend not specified. Falling back to '{self.attention_backend}' for deterministic inference. "
f"You can explicitly set --attention-backend to one of {DETERMINISTIC_ATTENTION_BACKEND_CHOICES}."
)
elif self.attention_backend not in DETERMINISTIC_ATTENTION_BACKEND_CHOICES:
# User explicitly specified an incompatible attention backend
raise ValueError(
f"Currently only {DETERMINISTIC_ATTENTION_BACKEND_CHOICES} attention backends are supported for deterministic inference, "
f"but you explicitly specified '{self.attention_backend}'."
)
if is_deepseek_model:
if self.attention_backend not in ["fa3", "triton"]:
raise ValueError(
f"Currently only {RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND} attention backends are supported for deterministic inference with DeepSeek models. But you're using {self.attention_backend}."
)
if (
self.attention_backend
not in RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND
):
# Currently, only certain backends support radix cache. Support for other backends is in progress
self.disable_radix_cache = True
logger.warning(
f"Currently radix cache is not compatible with {self.attention_backend} attention backend for deterministic inference. It will be supported in the future."
)
# Check TP size
if self.tp_size > 1:
if is_hip():
# AMD: use 1-stage all-reduce kernel which is inherently deterministic
# (each GPU reads all data from all GPUs, reduces locally in fixed order)
logger.info(
"AMD/ROCm: Using 1-stage all-reduce kernel (deterministic)"
)
else:
# CUDA: use NCCL tree algorithm
os.environ["NCCL_ALGO"] = "allreduce:tree"
self.disable_custom_all_reduce = True
logger.warning(
"NCCL_ALGO is set to 'allreduce:tree' and custom all reduce is disabled for deterministic inference when TP size > 1."
)
def _handle_dllm_inference(self):
if self.dllm_algorithm is None:
return
# On AMD/HIP, disable cuda graph for DLLM and use triton backend
if is_hip():
if not self.disable_cuda_graph:
logger.warning(
"Cuda graph is disabled for diffusion LLM inference on AMD GPUs"
)
self.disable_cuda_graph = True
if self.attention_backend not in ["triton", "aiter"]:
logger.warning(
"Attention backend is set to triton for diffusion LLM inference on AMD GPUs"
)
self.attention_backend = "triton"
elif is_npu():
if self.attention_backend != "ascend":
logger.warning(
"Attention backend is overridden to 'ascend' when running on NPU for diffusion LLM inference."
)
self.attention_backend = "ascend"
elif not self.disable_cuda_graph:
if self.attention_backend != "flashinfer":
logger.warning(
"Attention backend is set to flashinfer because of enabling cuda graph in diffusion LLM inference"
)
self.attention_backend = "flashinfer"
if not self.disable_overlap_schedule:
logger.warning(
"Overlap schedule is disabled because of using diffusion LLM inference"
)
self.disable_overlap_schedule = True
if not self.disable_radix_cache:
from sglang.srt.dllm.config import DllmConfig
config = DllmConfig.from_server_args(self)
if self.page_size % config.block_size != 0:
logger.warning(
f"Setting page size to {config.block_size} for diffusion LLM inference"
)
self.page_size = config.block_size
if self.enable_hierarchical_cache:
logger.warning(
"Hierarchical cache is disabled because of using diffusion LLM inference"
)
self.enable_hierarchical_cache = False
if self.enable_lmcache:
logger.warning(
"LMCache is disabled because of using diffusion LLM inference"
)
self.enable_lmcache = False
if not self.pp_size > 1:
logger.warning(
"Pipeline parallelism is disabled because of using diffusion LLM inference"
)
self.pp_size = 1
if self.enable_lora:
logger.warning(
"Currently LoRA is not supported by diffusion LLM inference."
)
self.enable_lora = False
if self.disaggregation_mode != "null":
logger.warning(
"Currently disaggregation is not supported by diffusion LLM inference."
)
self.disaggregation_mode = "null"
if self.enable_mixed_chunk:
logger.warning(
"Mixed chunked prefill is disabled because of using diffusion LLM inference."
)
self.enable_mixed_chunk = False
def _handle_other_validations(self):
# Handle model inference tensor dump.
if self.debug_tensor_dump_output_folder is not None:
logger.warning(
"Cuda graph and server warmup are disabled because of using tensor dump mode"
)
self.disable_cuda_graph = True
self.skip_server_warmup = True
if self.msprobe_dump_config is not None:
logger.warning(
"When msProbe is enabled, "
"cuda graph is disabled(disable_cuda_graph=True) because msProbe only supports dump in eager mode, "
"warmup is disabled(skip_server_warmup=True) because there is no need to dump data for this stage."
)
self.disable_cuda_graph = True
self.skip_server_warmup = True
# Validate limit_mm_per_prompt modalities
if self.limit_mm_data_per_request:
if isinstance(self.limit_mm_data_per_request, str):
self.limit_mm_data_per_request = json.loads(
self.limit_mm_data_per_request
)
if isinstance(self.limit_mm_data_per_request, dict):
allowed_modalities = {"image", "video", "audio"}
for modality in self.limit_mm_data_per_request.keys():
if modality not in allowed_modalities:
raise ValueError(
f"Invalid modality '{modality}' in --limit-mm-data-per-request."
f"Allowed modalities are: {list(allowed_modalities)}"
)
# Validate preferred_sampling_params
if self.preferred_sampling_params:
if isinstance(self.preferred_sampling_params, str):
self.preferred_sampling_params = json.loads(
self.preferred_sampling_params
)
def _handle_debug_utils(self):
if is_in_ci() and self.soft_watchdog_timeout is None:
logger.info("Set soft_watchdog_timeout since in CI")
self.soft_watchdog_timeout = 300
@staticmethod
def add_cli_args(parser: argparse.ArgumentParser):
# Model and tokenizer
parser.add_argument(
"--model-path",
"--model",
type=str,
help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
required=True,
)
parser.add_argument(
"--tokenizer-path",
type=str,
default=ServerArgs.tokenizer_path,
help="The path of the tokenizer.",
)
parser.add_argument(
"--tokenizer-mode",
type=str,
default=ServerArgs.tokenizer_mode,
choices=["auto", "slow"],
help="Tokenizer mode. 'auto' will use the fast "
"tokenizer if available, and 'slow' will "
"always use the slow tokenizer.",
)
parser.add_argument(
"--tokenizer-backend",
type=str,
default=ServerArgs.tokenizer_backend,
choices=["huggingface", "fastokens"],
help="Tokenizer backend. 'huggingface' uses the default HuggingFace "
"tokenizers library, and 'fastokens' uses the fastokens library "
"for faster tokenization. Requires the fastokens package to be installed.",
)
parser.add_argument(
"--tokenizer-worker-num",
type=int,
default=ServerArgs.tokenizer_worker_num,
help="The worker num of the tokenizer manager.",
)
parser.add_argument(
"--skip-tokenizer-init",
action="store_true",
help="If set, skip init tokenizer and pass input_ids in generate request.",
)
parser.add_argument(
"--load-format",
type=str,
default=ServerArgs.load_format,
choices=LOAD_FORMAT_CHOICES,
help="The format of the model weights to load. "
'"auto" will try to load the weights in the safetensors format '
"and fall back to the pytorch bin format if safetensors format "
"is not available. "
'"pt" will load the weights in the pytorch bin format. '
'"safetensors" will load the weights in the safetensors format. '
'"npcache" will load the weights in pytorch format and store '
"a numpy cache to speed up the loading. "
'"dummy" will initialize the weights with random values, '
"which is mainly for profiling."
'"gguf" will load the weights in the gguf format. '
'"bitsandbytes" will load the weights using bitsandbytes '
"quantization."
'"layered" loads weights layer by layer so that one can quantize a '
"layer before loading another to make the peak memory envelope "
"smaller.",
)
parser.add_argument(
"--model-loader-extra-config",
type=str,
help="Extra config for model loader. "
"This will be passed to the model loader corresponding to the chosen load_format.",
default=ServerArgs.model_loader_extra_config,
)
parser.add_argument(
"--trust-remote-code",
action="store_true",
help="Whether or not to allow for custom models defined on the Hub in their own modeling files.",
)
parser.add_argument(
"--context-length",
type=human_readable_int,
default=ServerArgs.context_length,
help="The model's maximum context length. Defaults to None (will use the value from the model's config.json instead)."
+ f"\n\n{human_readable_int.__doc__}",
)
parser.add_argument(
"--is-embedding",
action="store_true",
help="Whether to use a CausalLM as an embedding model.",
)
parser.add_argument(
"--enable-multimodal",
default=ServerArgs.enable_multimodal,
action="store_true",
help="Enable the multimodal functionality for the served model. If the model being served is not multimodal, nothing will happen",
)
parser.add_argument(
"--revision",
type=str,
default=None,
help="The specific model version to use. It can be a branch "
"name, a tag name, or a commit id. If unspecified, will use "
"the default version.",
)
parser.add_argument(
"--model-impl",
type=str,
default=ServerArgs.model_impl,
help="Which implementation of the model to use.\n\n"
'* "auto" will try to use the SGLang implementation if it exists '
"and fall back to the Transformers implementation if no SGLang "
"implementation is available.\n"
'* "sglang" will use the SGLang model implementation.\n'
'* "transformers" will use the Transformers model '
'* "mindspore" will use the MindSpore model '
"implementation.\n",
)
# HTTP server
parser.add_argument(
"--host",
type=str,
default=ServerArgs.host,
help="The host of the HTTP server.",
)
parser.add_argument(
"--port",
type=int,
default=ServerArgs.port,
help="The port of the HTTP server.",
)
parser.add_argument(
"--fastapi-root-path",
type=str,
default=ServerArgs.fastapi_root_path,
help="App is behind a path based routing proxy.",
)
parser.add_argument(
"--grpc-mode",
action="store_true",
help="If set, use gRPC server instead of HTTP server.",
)
parser.add_argument(
"--skip-server-warmup",
action="store_true",
help="If set, skip warmup.",
)
parser.add_argument(
"--warmups",
type=str,
required=False,
help="Specify custom warmup functions (csv) to run before server starts eg. --warmups=warmup_name1,warmup_name2 "
"will run the functions `warmup_name1` and `warmup_name2` specified in warmup.py before the server starts listening for requests",
)
parser.add_argument(
"--nccl-port",
type=int,
default=ServerArgs.nccl_port,
help="The port for NCCL distributed environment setup. Defaults to a random port.",
)
parser.add_argument(
"--checkpoint-engine-wait-weights-before-ready",
action="store_true",
help="If set, the server will wait for initial weights to be loaded via checkpoint-engine or other update methods "
"before serving inference requests.",
)
# SSL/TLS
parser.add_argument(
"--ssl-keyfile",
type=str,
default=ServerArgs.ssl_keyfile,
help="The file path to the SSL key file.",
)
parser.add_argument(
"--ssl-certfile",
type=str,
default=ServerArgs.ssl_certfile,
help="The file path to the SSL certificate file.",
)
parser.add_argument(
"--ssl-ca-certs",
type=str,
default=ServerArgs.ssl_ca_certs,
help="The CA certificates file.",
)
parser.add_argument(
"--ssl-keyfile-password",
type=str,
default=ServerArgs.ssl_keyfile_password,
help="The password to decrypt the SSL keyfile.",
)
parser.add_argument(
"--enable-ssl-refresh",
action="store_true",
default=ServerArgs.enable_ssl_refresh,
help="Enable automatic SSL certificate hot-reloading when cert/key "
"files change on disk. Requires --ssl-certfile and --ssl-keyfile.",
)
parser.add_argument(
"--enable-http2",
action="store_true",
default=ServerArgs.enable_http2,
help="Use Granian instead of Uvicorn as the ASGI server, enabling HTTP/1.1 and "
"HTTP/2 auto-negotiation. Clients may use h2c (cleartext HTTP/2) or plain HTTP/1.1. "
"Requires 'pip install sglang[http2]'.",
)
# Quantization and data type
parser.add_argument(
"--dtype",
type=str,
default=ServerArgs.dtype,
choices=["auto", "half", "float16", "bfloat16", "float", "float32"],
help="Data type for model weights and activations.\n\n"
'* "auto" will use FP16 precision for FP32 and FP16 models, and '
"BF16 precision for BF16 models.\n"
'* "half" for FP16. Recommended for AWQ quantization.\n'
'* "float16" is the same as "half".\n'
'* "bfloat16" for a balance between precision and range.\n'
'* "float" is shorthand for FP32 precision.\n'
'* "float32" for FP32 precision.',
)
parser.add_argument(
"--quantization",
type=str,
default=ServerArgs.quantization,
choices=QUANTIZATION_CHOICES,
help="The quantization method.",
)
parser.add_argument(
"--quantization-param-path",
type=nullable_str,
default=None,
help="Path to the JSON file containing the KV cache "
"scaling factors. This should generally be supplied, when "
"KV cache dtype is FP8. Otherwise, KV cache scaling factors "
"default to 1.0, which may cause accuracy issues. ",
)
parser.add_argument(
"--kv-cache-dtype",
type=str,
default=ServerArgs.kv_cache_dtype,
choices=["auto", "fp8_e5m2", "fp8_e4m3", "bf16", "bfloat16", "fp4_e2m1"],
help='Data type for kv cache storage. "auto" will use model data type. "bf16" or "bfloat16" for BF16 KV cache. "fp8_e5m2" and "fp8_e4m3" are supported for CUDA 11.8+. "fp4_e2m1" (only mxfp4) is supported for CUDA 12.8+ and PyTorch 2.8.0+',
)
parser.add_argument(
"--enable-fp32-lm-head",
action="store_true",
help="If set, the LM head outputs (logits) are in FP32.",
)
parser.add_argument(
"--modelopt-quant",
type=str,
default=ServerArgs.modelopt_quant,
help="The ModelOpt quantization configuration. "
"Supported values: 'fp8', 'int4_awq', 'w4a8_awq', 'nvfp4', 'nvfp4_awq'. "
"This requires the NVIDIA Model Optimizer library to be installed: pip install nvidia-modelopt",
)
parser.add_argument(
"--modelopt-checkpoint-restore-path",
type=str,
default=ServerArgs.modelopt_checkpoint_restore_path,
help="Path to restore a previously saved ModelOpt quantized checkpoint. "
"If provided, the quantization process will be skipped and the model "
"will be loaded from this checkpoint.",
)
parser.add_argument(
"--modelopt-checkpoint-save-path",
type=str,
default=ServerArgs.modelopt_checkpoint_save_path,
help="Path to save the ModelOpt quantized checkpoint after quantization. "
"This allows reusing the quantized model in future runs.",
)
parser.add_argument(
"--modelopt-export-path",
type=str,
default=ServerArgs.modelopt_export_path,
help="Path to export the quantized model in HuggingFace format after ModelOpt quantization. "
"The exported model can then be used directly with SGLang for inference. "
"If not provided, the model will not be exported.",
)
parser.add_argument(
"--quantize-and-serve",
action="store_true",
default=ServerArgs.quantize_and_serve,
help="Quantize the model with ModelOpt and immediately serve it without exporting. "
"This is useful for development and prototyping. For production, it's recommended "
"to use separate quantization and deployment steps.",
)
parser.add_argument(
"--rl-quant-profile",
type=str,
default=ServerArgs.rl_quant_profile,
help="Path to the FlashRL quantization profile. Required when using --load-format flash_rl.",
)
# Memory and scheduling
parser.add_argument(
"--mem-fraction-static",
type=float,
default=ServerArgs.mem_fraction_static,
help="The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors.",
)
parser.add_argument(
"--max-running-requests",
type=int,
default=ServerArgs.max_running_requests,
help="The maximum number of running requests.",
)
parser.add_argument(
"--max-queued-requests",
type=int,
default=ServerArgs.max_queued_requests,
help="The maximum number of queued requests. This option is ignored when using disaggregation-mode.",
)
parser.add_argument(
"--max-total-tokens",
type=human_readable_int,
default=ServerArgs.max_total_tokens,
help="The maximum number of tokens in the memory pool. If not specified, it will be automatically calculated based on the memory usage fraction. "
"This option is typically used for development and debugging purposes."
+ f"\n\n{human_readable_int.__doc__}",
)
parser.add_argument(
"--chunked-prefill-size",
type=int,
default=ServerArgs.chunked_prefill_size,
help="The maximum number of tokens in a chunk for the chunked prefill. Setting this to -1 means disabling chunked prefill.",
)
parser.add_argument(
"--prefill-max-requests",
type=int,
default=ServerArgs.prefill_max_requests,
help="The maximum number of requests in a prefill batch. If not specified, there is no limit.",
)
parser.add_argument(
"--enable-dynamic-chunking",
action="store_true",
default=ServerArgs.enable_dynamic_chunking,
help="Enable dynamic chunk size adjustment for pipeline parallelism. When enabled, chunk sizes are dynamically calculated based on fitted function to maintain consistent execution time across chunks.",
)
parser.add_argument(
"--max-prefill-tokens",
type=human_readable_int,
default=ServerArgs.max_prefill_tokens,
help="The maximum number of tokens in a prefill batch. The real bound will be the maximum of this value and the model's maximum context length."
+ f"\n\n{human_readable_int.__doc__}",
)
parser.add_argument(
"--schedule-policy",
type=str,
default=ServerArgs.schedule_policy,
choices=[
"lpm",
"random",
"fcfs",
"dfs-weight",
"lof",
"priority",
"routing-key",
],
help="The scheduling policy of the requests.",
)
parser.add_argument(
"--enable-priority-scheduling",
action="store_true",
default=ServerArgs.enable_priority_scheduling,
help="Enable priority scheduling. Requests with higher priority integer values will be scheduled first by default.",
)
parser.add_argument(
"--disable-priority-preemption",
action="store_true",
default=ServerArgs.disable_priority_preemption,
help="Disable priority scheduling preemption.",
)
parser.add_argument(
"--default-priority-value",
type=int,
default=ServerArgs.default_priority_value,
help="Default priority for requests without explicit priority.",
)
parser.add_argument(
"--abort-on-priority-when-disabled",
action="store_true",
default=ServerArgs.abort_on_priority_when_disabled,
help="If set, abort requests that specify a priority when priority scheduling is disabled.",
)
parser.add_argument(
"--schedule-low-priority-values-first",
action="store_true",
default=ServerArgs.schedule_low_priority_values_first,
help="If specified with --enable-priority-scheduling, the scheduler will schedule requests with lower priority integer values first.",
)
parser.add_argument(
"--priority-scheduling-preemption-threshold",
type=int,
default=ServerArgs.priority_scheduling_preemption_threshold,
help="Minimum difference in priorities for an incoming request to have to preempt running request(s).",
)
parser.add_argument(
"--schedule-conservativeness",
type=float,
default=ServerArgs.schedule_conservativeness,
help="How conservative the schedule policy is. A larger value means more conservative scheduling. Use a larger value if you see requests being retracted frequently.",
)
parser.add_argument(
"--page-size",
type=int,
default=ServerArgs.page_size,
help="The number of tokens in a page.",
)
parser.add_argument(
"--hybrid-kvcache-ratio",
action=DeprecatedAction,
help="Note: --hybrid-kvcache-ratio is deprecated now. Please use --swa-full-tokens-ratio instead.",
)
parser.add_argument(
"--swa-full-tokens-ratio",
type=float,
default=ServerArgs.swa_full_tokens_ratio,
help="The ratio of SWA layer KV tokens / full layer KV tokens, regardless of the number of swa:full layers. It should be between 0 and 1. "
"E.g. 0.5 means if each swa layer has 50 tokens, then each full layer has 100 tokens.",
)
parser.add_argument(
"--disable-hybrid-swa-memory",
action="store_true",
help="Disable the hybrid SWA memory pool.",
)
parser.add_argument(
"--radix-eviction-policy",
type=str,
choices=RADIX_EVICTION_POLICY_CHOICES,
default=ServerArgs.radix_eviction_policy,
help="The eviction policy of radix trees. 'lru' stands for Least Recently Used, 'lfu' stands for Least Frequently Used, 'slru' stands for Segmented Least Recently Used, and 'priority' evicts lower-priority requests first.",
)
parser.add_argument(
"--enable-prefill-delayer",
action="store_true",
help="Enable prefill delayer for DP attention to reduce idle time.",
)
parser.add_argument(
"--prefill-delayer-max-delay-passes",
type=int,
default=ServerArgs.prefill_delayer_max_delay_passes,
help="Maximum forward passes to delay prefill.",
)
parser.add_argument(
"--prefill-delayer-token-usage-low-watermark",
type=float,
default=None,
help="Token usage low watermark for prefill delayer.",
)
parser.add_argument(
"--prefill-delayer-forward-passes-buckets",
type=float,
nargs="+",
default=None,
help="Custom buckets for prefill delayer forward passes histogram. 0 and max_delay_passes-1 will be auto-added.",
)
parser.add_argument(
"--prefill-delayer-wait-seconds-buckets",
type=float,
nargs="+",
default=None,
help="Custom buckets for prefill delayer wait seconds histogram. 0 will be auto-added.",
)
# Runtime options
parser.add_argument(
"--device",
type=str,
default=ServerArgs.device,
help="The device to use ('cuda', 'xpu', 'hpu', 'npu', 'cpu', 'musa'). Defaults to auto-detection if not specified.",
)
parser.add_argument(
"--tensor-parallel-size",
"--tp-size",
type=int,
default=ServerArgs.tp_size,
help="The tensor parallelism size.",
)
parser.add_argument(
"--attention-context-parallel-size",
"--attn-cp-size",
type=int,
default=ServerArgs.attn_cp_size,
help="The attention context parallelism size.",
)
parser.add_argument(
"--moe-data-parallel-size",
"--moe-dp-size",
type=int,
default=ServerArgs.moe_dp_size,
help="The moe data parallelism size.",
)
parser.add_argument(
"--pipeline-parallel-size",
"--pp-size",
type=int,
default=ServerArgs.pp_size,
help="The pipeline parallelism size.",
)
parser.add_argument(
"--pp-max-micro-batch-size",
type=int,
default=ServerArgs.pp_max_micro_batch_size,
help="The maximum micro batch size in pipeline parallelism.",
)
parser.add_argument(
"--pp-async-batch-depth",
type=int,
default=ServerArgs.pp_async_batch_depth,
help="The async batch depth of pipeline parallelism.",
)
parser.add_argument(
"--stream-interval",
type=int,
default=ServerArgs.stream_interval,
help="The interval (or buffer size) for streaming in terms of the token length. A smaller value makes streaming smoother, while a larger value makes the throughput higher",
)
parser.add_argument(
"--batch-notify-size",
type=int,
default=ServerArgs.batch_notify_size,
help="Number of streaming notifications to batch before yielding to the event loop. "
"Reduces asyncio wakeup overhead under high concurrency.",
)
parser.add_argument(
"--incremental-streaming-output",
action="store_true",
help="Whether to output as a sequence of disjoint segments.",
)
parser.add_argument(
"--stream-response-default-include-usage",
action="store_true",
help="Include usage in every streaming response "
"(even when stream_options is not specified).",
)
parser.add_argument(
"--stream-output",
action=DeprecatedStoreTrueAction,
dest="incremental_streaming_output",
new_flag="--incremental-streaming-output",
help="[Deprecated] Use --incremental-streaming-output instead.",
)
parser.add_argument(
"--enable-streaming-session",
action="store_true",
default=ServerArgs.enable_streaming_session,
help="Enable streaming session mode and StreamingSession wrapper.",
)
parser.add_argument(
"--random-seed",
type=int,
default=ServerArgs.random_seed,
help="The random seed.",
)
parser.add_argument(
"--constrained-json-whitespace-pattern",
type=str,
default=ServerArgs.constrained_json_whitespace_pattern,
help="(outlines and llguidance backends only) Regex pattern for syntactic whitespaces allowed in JSON constrained output. For example, to allow the model generate consecutive whitespaces, set the pattern to [\n\t ]*",
)
parser.add_argument(
"--constrained-json-disable-any-whitespace",
action="store_true",
help="(xgrammar and llguidance backends only) Enforce compact representation in JSON constrained output.",
)
parser.add_argument(
"--watchdog-timeout",
type=float,
default=ServerArgs.watchdog_timeout,
help="Set watchdog timeout in seconds. If a forward batch takes longer than this, the server will crash to prevent hanging.",
)
parser.add_argument(
"--soft-watchdog-timeout",
type=float,
default=ServerArgs.soft_watchdog_timeout,
help="Set soft watchdog timeout in seconds. If a forward batch takes longer than this, the server will dump information for debugging.",
)
parser.add_argument(
"--dist-timeout",
type=int,
default=ServerArgs.dist_timeout,
help="Set timeout for torch.distributed initialization.",
)
parser.add_argument(
"--download-dir",
type=str,
default=ServerArgs.download_dir,
help="Model download directory for huggingface.",
)
parser.add_argument(
"--model-checksum",
type=str,
nargs="?",
const="",
default=None,
help="Model file integrity verification. If provided without value, uses model-path as HF repo ID. Otherwise, provide checksums JSON file path or HuggingFace repo ID.",
)
parser.add_argument(
"--base-gpu-id",
type=int,
default=ServerArgs.base_gpu_id,
help="The base GPU ID to start allocating GPUs from. Useful when running multiple instances on the same machine.",
)
parser.add_argument(
"--gpu-id-step",
type=int,
default=ServerArgs.gpu_id_step,
help="The delta between consecutive GPU IDs that are used. For example, setting it to 2 will use GPU 0,2,4,...",
)
parser.add_argument(
"--sleep-on-idle",
action="store_true",
help="Reduce CPU usage when sglang is idle.",
)
parser.add_argument(
"--use-ray",
action="store_true",
help="Use Ray actors for scheduler process management.",
)
parser.add_argument(
"--custom-sigquit-handler",
help="Register a custom sigquit handler so you can do additional cleanup after the server is shutdown. This is only available for Engine, not for CLI.",
)
# Logging
parser.add_argument(
"--log-level",
type=str,
default=ServerArgs.log_level,
help="The logging level of all loggers.",
)
parser.add_argument(
"--log-level-http",
type=str,
default=ServerArgs.log_level_http,
help="The logging level of HTTP server. If not set, reuse --log-level by default.",
)
parser.add_argument(
"--log-requests",
action="store_true",
help="Log metadata, inputs, outputs of all requests. The verbosity is decided by --log-requests-level",
)
parser.add_argument(
"--log-requests-level",
type=int,
default=ServerArgs.log_requests_level,
help="0: Log metadata (no sampling parameters). 1: Log metadata and sampling parameters. 2: Log metadata, sampling parameters and partial input/output. 3: Log every input/output.",
choices=[0, 1, 2, 3],
)
parser.add_argument(
"--log-requests-format",
type=str,
default=ServerArgs.log_requests_format,
choices=["text", "json"],
help="Format for request logging: 'text' (human-readable) or 'json' (structured)",
)
parser.add_argument(
"--log-requests-target",
type=str,
nargs="+",
default=ServerArgs.log_requests_target,
help="Target(s) for request logging: 'stdout' and/or directory path(s) for file output. "
"Can specify multiple targets, e.g., '--log-requests-target stdout /my/path'. ",
)
parser.add_argument(
"--uvicorn-access-log-exclude-prefixes",
type=str,
nargs="*",
default=list(DEFAULT_UVICORN_ACCESS_LOG_EXCLUDE_PREFIXES),
help="Exclude uvicorn access logs whose request path starts with any of these prefixes. "
"Defaults to empty (disabled). "
"Example: --uvicorn-access-log-exclude-prefixes /metrics /health",
)
parser.add_argument(
"--crash-dump-folder",
type=str,
default=ServerArgs.crash_dump_folder,
help="Folder path to dump requests from the last 5 min before a crash (if any). If not specified, crash dumping is disabled.",
)
parser.add_argument(
"--show-time-cost",
action="store_true",
help="Show time cost of custom marks.",
)
parser.add_argument(
"--enable-metrics",
action="store_true",
help="Enable log prometheus metrics.",
)
parser.add_argument(
"--grpc-http-sidecar-port",
type=int,
default=ServerArgs.grpc_http_sidecar_port,
help="Port for the HTTP sidecar server in gRPC mode (--grpc-mode). "
"Serves Prometheus metrics and profiling endpoints. "
"Defaults to --port + 1. Not used in HTTP mode.",
)
parser.add_argument(
"--enable-mfu-metrics",
action="store_true",
help="Enable estimated MFU-related prometheus metrics.",
)
parser.add_argument(
"--enable-metrics-for-all-schedulers",
action="store_true",
help="Enable --enable-metrics-for-all-schedulers when you want schedulers on all TP ranks (not just TP 0) "
"to record request metrics separately. This is especially useful when dp_attention is enabled, as "
"otherwise all metrics appear to come from TP 0.",
)
parser.add_argument(
"--tokenizer-metrics-custom-labels-header",
type=str,
default=ServerArgs.tokenizer_metrics_custom_labels_header,
help="Specify the HTTP header for passing custom labels for tokenizer metrics.",
)
parser.add_argument(
"--tokenizer-metrics-allowed-custom-labels",
type=str,
nargs="+",
default=ServerArgs.tokenizer_metrics_allowed_custom_labels,
help="The custom labels allowed for tokenizer metrics. The labels are specified via a dict in "
"'--tokenizer-metrics-custom-labels-header' field in HTTP requests, e.g., {'label1': 'value1', 'label2': "
"'value2'} is allowed if '--tokenizer-metrics-allowed-custom-labels label1 label2' is set.",
)
parser.add_argument(
"--extra-metric-labels",
type=json.loads,
default=ServerArgs.extra_metric_labels,
help="The custom labels for metrics. "
'e.g. \'{"label1": "value1", "label2": "value2"}\'',
)
parser.add_argument(
"--bucket-time-to-first-token",
type=float,
nargs="+",
default=ServerArgs.bucket_time_to_first_token,
help="The buckets of time to first token, specified as a list of floats.",
)
parser.add_argument(
"--bucket-inter-token-latency",
type=float,
nargs="+",
default=ServerArgs.bucket_inter_token_latency,
help="The buckets of inter-token latency, specified as a list of floats.",
)
parser.add_argument(
"--bucket-e2e-request-latency",
type=float,
nargs="+",
default=ServerArgs.bucket_e2e_request_latency,
help="The buckets of end-to-end request latency, specified as a list of floats.",
)
parser.add_argument(
"--collect-tokens-histogram",
action=DeprecatedAction,
help="Deprecated. Token histograms are now automatically collected when --enable-metrics is set.",
)
bucket_rule = (
"Supports 3 rule types: 'default' uses predefined buckets; 'tse <middle> <base> <count>' "
"generates two sides exponential distributed buckets (e.g., 'tse 1000 2 8' generates buckets "
"[984.0, 992.0, 996.0, 998.0, 1000.0, 1002.0, 1004.0, 1008.0, 1016.0]).); 'custom <value1> "
"<value2> ...' uses custom bucket values (e.g., 'custom 10 50 100 500')."
)
parser.add_argument(
"--prompt-tokens-buckets",
type=str,
nargs="+",
default=ServerArgs.prompt_tokens_buckets,
help=f"The buckets rule of prompt tokens. {bucket_rule}",
)
parser.add_argument(
"--generation-tokens-buckets",
type=str,
nargs="+",
default=ServerArgs.generation_tokens_buckets,
help=f"The buckets rule for generation tokens histogram. {bucket_rule}",
)
parser.add_argument(
"--gc-warning-threshold-secs",
type=float,
default=ServerArgs.gc_warning_threshold_secs,
help="The threshold for long GC warning. If a GC takes longer than this, a warning will be logged. Set to 0 to disable.",
)
parser.add_argument(
"--decode-log-interval",
type=int,
default=ServerArgs.decode_log_interval,
help="The log and metrics reporting interval (in decode iterations) for decode batches.",
)
parser.add_argument(
"--enable-request-time-stats-logging",
action="store_true",
default=ServerArgs.enable_request_time_stats_logging,
help="Enable per request time stats logging",
)
parser.add_argument(
"--kv-events-config",
type=str,
default=None,
help="Config in json format for NVIDIA dynamo KV event publishing. Publishing will be enabled if this flag is used.",
)
parser.add_argument(
"--enable-trace",
action="store_true",
help="Enable opentelemetry trace",
)
parser.add_argument(
"--otlp-traces-endpoint",
type=str,
default="localhost:4317",
help="Config opentelemetry collector endpoint if --enable-trace is set. format: <ip>:<port>",
)
# RequestMetricsExporter configuration
parser.add_argument(
"--export-metrics-to-file",
action="store_true",
help="Export performance metrics for each request to local file (e.g. for forwarding to external systems).",
)
parser.add_argument(
"--export-metrics-to-file-dir",
type=str,
default=ServerArgs.export_metrics_to_file_dir,
help="Directory path for writing performance metrics files (required when --export-metrics-to-file is enabled).",
)
# API related
parser.add_argument(
"--api-key",
type=str,
default=ServerArgs.api_key,
help="Set API key of the server. It is also used in the OpenAI API compatible server.",
)
parser.add_argument(
"--admin-api-key",
type=str,
default=ServerArgs.admin_api_key,
help=(
"Set admin API key for sensitive management endpoints (e.g. /clear_hicache_storage_backend). "
"When set, admin endpoints require this key and do NOT accept --api-key."
),
)
parser.add_argument(
"--served-model-name",
type=str,
default=ServerArgs.served_model_name,
help="Override the model name returned by the v1/models endpoint in OpenAI API server.",
)
parser.add_argument(
"--weight-version",
type=str,
default=ServerArgs.weight_version,
help="Version identifier for the model weights. Defaults to 'default' if not specified.",
)
parser.add_argument(
"--chat-template",
type=str,
default=ServerArgs.chat_template,
help="The buliltin chat template name or the path of the chat template file. This is only used for OpenAI-compatible API server.",
)
parser.add_argument(
"--hf-chat-template-name",
type=str,
default=ServerArgs.hf_chat_template_name,
help="When the HuggingFace tokenizer has multiple chat templates (e.g., 'default', 'tool_use', 'rag'), "
"specify which named template to use. If not set, the first available template is used.",
)
parser.add_argument(
"--completion-template",
type=str,
default=ServerArgs.completion_template,
help="The buliltin completion template name or the path of the completion template file. This is only used for OpenAI-compatible API server. only for code completion currently.",
)
parser.add_argument(
"--file-storage-path",
type=str,
default=ServerArgs.file_storage_path,
help="The path of the file storage in backend.",
)
parser.add_argument(
"--enable-cache-report",
action="store_true",
help="Return number of cached tokens in usage.prompt_tokens_details for each openai request.",
)
parser.add_argument(
"--reasoning-parser",
type=str,
choices=list(ReasoningParser.DetectorMap.keys()),
default=ServerArgs.reasoning_parser,
help=f"Specify the parser for reasoning models, supported parsers are: {list(ReasoningParser.DetectorMap.keys())}.",
)
parser.add_argument(
"--strip-thinking-cache",
action="store_true",
help="Skip caching reasoning-model output (thinking + answer) in the "
"radix tree on finish; keep only the prompt prefix. Opt-in: changes "
"cache contents.",
)
tool_call_parser_choices = list(FunctionCallParser.ToolCallParserEnum.keys())
parser.add_argument(
"--tool-call-parser",
type=str,
choices=tool_call_parser_choices,
default=ServerArgs.tool_call_parser,
help=f"Specify the parser for handling tool-call interactions. Options include: {tool_call_parser_choices}.",
)
parser.add_argument(
"--tool-server",
type=str,
default=None,
help="Either 'demo' or a comma-separated list of tool server urls to use for the model. If not specified, no tool server will be used.",
)
parser.add_argument(
"--sampling-defaults",
type=str,
choices=["openai", "model"],
default=ServerArgs.sampling_defaults,
help="Where to get default sampling parameters. "
"'openai' uses SGLang/OpenAI defaults (temperature=1.0, top_p=1.0, etc.). "
"'model' uses the model's generation_config.json to get the recommended "
"sampling parameters if available. Default is 'model'.",
)
# Data parallelism
parser.add_argument(
"--data-parallel-size",
"--dp-size",
type=int,
default=ServerArgs.dp_size,
help="The data parallelism size.",
)
parser.add_argument(
"--load-balance-method",
type=str,
default=ServerArgs.load_balance_method,
help="The load balancing strategy for data parallelism.",
choices=[
"auto",
"round_robin",
"follow_bootstrap_room",
"total_requests",
"total_tokens",
],
)
parser.add_argument(
"--prefill-round-robin-balance",
action=DeprecatedAction,
help="Note: --prefill-round-robin-balance is deprecated now.",
)
# Multi-node distributed serving
parser.add_argument(
"--dist-init-addr",
"--nccl-init-addr", # For backward compatibility. This will be removed in the future.
type=str,
help="The host address for initializing distributed backend (e.g., `192.168.0.2:25000`).",
)
parser.add_argument(
"--nnodes", type=int, default=ServerArgs.nnodes, help="The number of nodes."
)
parser.add_argument(
"--node-rank", type=int, default=ServerArgs.node_rank, help="The node rank."
)
# Model override args
parser.add_argument(
"--json-model-override-args",
type=str,
help="A dictionary in JSON string format used to override default model configurations.",
default=ServerArgs.json_model_override_args,
)
parser.add_argument(
"--preferred-sampling-params",
type=json.loads,
help="json-formatted sampling settings that will be returned in /get_model_info",
)
# LoRA
parser.add_argument(
"--enable-lora",
default=ServerArgs.enable_lora,
action="store_true",
help="Enable LoRA support for the model. This argument is automatically set to True if `--lora-paths` is provided for backward compatibility.",
)
parser.add_argument(
"--enable-lora-overlap-loading",
default=ServerArgs.enable_lora_overlap_loading,
action="store_true",
help="Enable asynchronous LoRA weight loading in order to overlap H2D transfers with GPU compute. This should be enabled if you find that your LoRA workloads are bottlenecked by adapter weight loading, for example when frequently loading large LoRA adapters.",
)
parser.add_argument(
"--max-lora-rank",
default=ServerArgs.max_lora_rank,
type=int,
help="The maximum rank of LoRA adapters. If not specified, it will be automatically inferred from the adapters provided in --lora-paths.",
)
parser.add_argument(
"--lora-target-modules",
type=str,
choices=SUPPORTED_LORA_TARGET_MODULES + [LORA_TARGET_ALL_MODULES],
nargs="*",
default=None,
help="The union set of all target modules where LoRA should be applied. If not specified, "
"it will be automatically inferred from the adapters provided in --lora-paths. If 'all' is specified, "
"all supported modules will be targeted.",
)
parser.add_argument(
"--lora-paths",
type=str,
nargs="*",
default=None,
action=LoRAPathAction,
help='The list of LoRA adapters to load. Each adapter must be specified in one of the following formats: <PATH> | <NAME>=<PATH> | JSON with schema {"lora_name":str,"lora_path":str,"pinned":bool}',
)
parser.add_argument(
"--max-loras-per-batch",
type=int,
default=8,
help="Maximum number of adapters for a running batch, include base-only request.",
)
parser.add_argument(
"--max-loaded-loras",
type=int,
default=ServerArgs.max_loaded_loras,
help="If specified, it limits the maximum number of LoRA adapters loaded in CPU memory at a time. The value must be greater than or equal to `--max-loras-per-batch`.",
)
parser.add_argument(
"--lora-eviction-policy",
type=str,
default=ServerArgs.lora_eviction_policy,
choices=["lru", "fifo"],
help="LoRA adapter eviction policy when memory pool is full. 'lru': Least Recently Used (default, better cache efficiency). 'fifo': First-In-First-Out.",
)
parser.add_argument(
"--lora-backend",
type=str,
choices=LORA_BACKEND_CHOICES,
default=ServerArgs.lora_backend,
help="Choose the kernel backend for multi-LoRA serving.",
)
parser.add_argument(
"--max-lora-chunk-size",
type=int,
default=ServerArgs.max_lora_chunk_size,
choices=[16, 32, 64, 128],
help="Maximum chunk size for the ChunkedSGMV LoRA backend. Only used when --lora-backend is 'csgmv'. Choosing a larger value might improve performance.",
)
parser.add_argument(
"--experts-shared-outer-loras",
default=ServerArgs.experts_shared_outer_loras,
action=argparse.BooleanOptionalAction,
help="Force shared outer LoRA mode for MoE models. "
"When set, w1/w3 lora_A and w2 lora_B are shared across experts "
"(expert_dim=1). Use --no-experts-shared-outer-loras to force disable. "
"By default this is auto-detected from adapter weights.",
)
parser.add_argument(
"--lora-use-virtual-experts",
default=ServerArgs.lora_use_virtual_experts,
action="store_true",
help="Enable virtual expert computation for MoE models. When set, the model will use virtual expert computation.",
)
parser.add_argument(
"--lora-strict-loading",
default=ServerArgs.lora_strict_loading,
action=argparse.BooleanOptionalAction,
help="Enable strict loading for LoRA adapters. "
"When set, mismatched or missing keys in the adapter weights will raise an error.",
)
# Kernel backend
parser.add_argument(
"--attention-backend",
type=str,
choices=ATTENTION_BACKEND_CHOICES,
default=ServerArgs.attention_backend,
help="Choose the kernels for attention layers.",
)
parser.add_argument(
"--prefill-attention-backend",
type=str,
choices=ATTENTION_BACKEND_CHOICES,
default=ServerArgs.prefill_attention_backend,
help="Choose the kernels for prefill attention layers (have priority over --attention-backend).",
)
parser.add_argument(
"--decode-attention-backend",
type=str,
choices=ATTENTION_BACKEND_CHOICES,
default=ServerArgs.decode_attention_backend,
help="Choose the kernels for decode attention layers (have priority over --attention-backend).",
)
parser.add_argument(
"--sampling-backend",
type=str,
choices=SAMPLING_BACKEND_CHOICES,
default=ServerArgs.sampling_backend,
help="Choose the kernels for sampling layers.",
)
parser.add_argument(
"--grammar-backend",
type=str,
choices=GRAMMAR_BACKEND_CHOICES,
default=ServerArgs.grammar_backend,
help="Choose the backend for grammar-guided decoding.",
)
parser.add_argument(
"--mm-attention-backend",
type=str,
choices=[
"sdpa",
"fa3",
"fa4",
"triton_attn",
"ascend_attn",
"aiter_attn",
"flashinfer_cudnn",
],
default=ServerArgs.mm_attention_backend,
help="Set multimodal attention backend.",
)
parser.add_argument(
"--nsa-prefill-backend",
default=ServerArgs.nsa_prefill_backend,
type=str,
choices=NSA_CHOICES,
help="NSA prefill backend. If not specified, auto-detects based on hardware and kv_cache_dtype.",
)
parser.add_argument(
"--nsa-decode-backend",
default=ServerArgs.nsa_decode_backend,
type=str,
choices=NSA_CHOICES,
help="NSA decode backend. If not specified, auto-detects based on hardware and kv_cache_dtype.",
)
parser.add_argument(
"--fp8-gemm-backend",
type=str,
choices=FP8_GEMM_RUNNER_BACKEND_CHOICES,
default=ServerArgs.fp8_gemm_runner_backend,
dest="fp8_gemm_runner_backend",
help="Choose the runner backend for Blockwise FP8 GEMM operations. "
"Options: 'auto' (default, auto-selects based on hardware), "
"'deep_gemm' (JIT-compiled; enabled by default on NVIDIA Hopper (SM90) and Blackwell (SM100) when DeepGEMM is installed), "
"'flashinfer_trtllm' (optimal for Blackwell and low-latency), "
"'flashinfer_cutlass' (FlashInfer CUTLASS groupwise FP8 GEMM), "
"'flashinfer_deepgemm' (Hopper SM90 only; uses swapAB optimization for small M dimensions in decoding), "
"'cutlass' (optimal for Hopper/Blackwell GPUs and high-throughput), "
"'triton' (fallback, widely compatible), "
"'aiter' (ROCm only). ",
)
parser.add_argument(
"--fp4-gemm-backend",
type=str,
choices=FP4_GEMM_RUNNER_BACKEND_CHOICES,
default=ServerArgs.fp4_gemm_runner_backend,
dest="fp4_gemm_runner_backend",
help="Choose the runner backend for NVFP4 GEMM operations. "
"Options: 'auto' (default; selects flashinfer_cudnn on SM120, flashinfer_cutlass otherwise), "
"'cutlass' (SGLang CUTLASS kernel), "
"'flashinfer_cutlass' (FlashInfer CUTLASS backend), "
"'flashinfer_cudnn' (FlashInfer cuDNN backend, optimal on CUDA 13+ with cuDNN 9.15+), "
"'flashinfer_trtllm' (FlashInfer TensorRT-LLM backend, requires different weight preparation with shuffling). ",
)
parser.add_argument(
"--disable-flashinfer-autotune",
default=ServerArgs.disable_flashinfer_autotune,
action="store_true",
help="Disable FlashInfer autotuning.",
)
# Speculative decoding
parser.add_argument(
"--speculative-algorithm",
type=str,
choices=["DFLASH", "EAGLE", "EAGLE3", "NEXTN", "STANDALONE", "NGRAM"],
help="Speculative algorithm.",
)
parser.add_argument(
"--speculative-draft-model-path",
"--speculative-draft-model",
type=str,
help="The path of the draft model weights. This can be a local folder or a Hugging Face repo ID.",
)
parser.add_argument(
"--speculative-draft-model-revision",
type=str,
default=None,
help="The specific draft model version to use. It can be a branch "
"name, a tag name, or a commit id. If unspecified, will use "
"the default version.",
)
parser.add_argument(
"--speculative-draft-load-format",
type=str,
default=ServerArgs.speculative_draft_load_format,
choices=LOAD_FORMAT_CHOICES,
help="The format of the draft model weights to load. "
"If not specified, will use the same format as --load-format. "
"Use 'dummy' to initialize draft model weights with random values for profiling.",
)
parser.add_argument(
"--speculative-num-steps",
type=int,
help="The number of steps sampled from draft model in Speculative Decoding.",
default=ServerArgs.speculative_num_steps,
)
parser.add_argument(
"--speculative-eagle-topk",
type=int,
help="The number of tokens sampled from the draft model in eagle2 each step.",
default=ServerArgs.speculative_eagle_topk,
)
parser.add_argument(
"--speculative-num-draft-tokens",
type=int,
help="The number of tokens sampled from the draft model in Speculative Decoding.",
default=ServerArgs.speculative_num_draft_tokens,
)
parser.add_argument(
"--speculative-dflash-block-size",
type=int,
help="DFLASH only. Block size (verify window length). Alias of --speculative-num-draft-tokens for DFLASH.",
default=ServerArgs.speculative_dflash_block_size,
)
parser.add_argument(
"--speculative-dflash-draft-window-size",
type=int,
help="DFLASH only. Sliding window size for the draft-model KV cache. "
"When set, the draft worker keeps a recent target-token window in its "
"local cache (paged backends may retain up to one extra page on the left "
"for alignment). Default is full context.",
default=ServerArgs.speculative_dflash_draft_window_size,
)
parser.add_argument(
"--speculative-accept-threshold-single",
type=float,
help="Accept a draft token if its probability in the target model is greater than this threshold.",
default=ServerArgs.speculative_accept_threshold_single,
)
parser.add_argument(
"--speculative-accept-threshold-acc",
type=float,
help="The accept probability of a draft token is raised from its target probability p to min(1, p / threshold_acc).",
default=ServerArgs.speculative_accept_threshold_acc,
)
parser.add_argument(
"--speculative-token-map",
type=str,
help="The path of the draft model's small vocab table.",
default=ServerArgs.speculative_token_map,
)
parser.add_argument(
"--speculative-attention-mode",
type=str,
choices=["prefill", "decode"],
help="Attention backend for speculative decoding operations (both target verify and draft extend). Can be one of 'prefill' (default) or 'decode'.",
default=ServerArgs.speculative_attention_mode,
)
parser.add_argument(
"--speculative-draft-attention-backend",
type=str,
help="Attention backend for speculative decoding drafting.",
default=ServerArgs.speculative_draft_attention_backend,
)
parser.add_argument(
"--speculative-moe-runner-backend",
type=str,
choices=MOE_RUNNER_BACKEND_CHOICES,
default=ServerArgs.speculative_moe_runner_backend,
help="Choose the runner backend for MoE in speculative decoding.",
)
parser.add_argument(
"--speculative-moe-a2a-backend",
type=str,
choices=MOE_A2A_BACKEND_CHOICES,
default=ServerArgs.speculative_moe_a2a_backend,
help="Choose the backend for MoE A2A in speculative decoding",
)
parser.add_argument(
"--speculative-draft-model-quantization",
type=str,
choices=SPECULATIVE_DRAFT_MODEL_QUANTIZATION_CHOICES,
default=ServerArgs.speculative_draft_model_quantization,
help="The quantization method for speculative model.",
)
# Speculative decoding (ngram)
parser.add_argument(
"--speculative-ngram-min-bfs-breadth",
type=int,
default=ServerArgs.speculative_ngram_min_bfs_breadth,
help="The minimum breadth for BFS (Breadth-First Search) in ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-max-bfs-breadth",
type=int,
default=ServerArgs.speculative_ngram_max_bfs_breadth,
help="The maximum breadth for BFS (Breadth-First Search) in ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-match-type",
type=str,
choices=["BFS", "PROB"],
default=ServerArgs.speculative_ngram_match_type,
help="The match type for cache tree.",
)
parser.add_argument(
"--speculative-ngram-max-trie-depth",
type=int,
default=ServerArgs.speculative_ngram_max_trie_depth,
help="The max trie depth for ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-capacity",
type=int,
default=ServerArgs.speculative_ngram_capacity,
help="The cache capacity for ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-external-corpus-path",
type=str,
default=ServerArgs.speculative_ngram_external_corpus_path,
help="Path to an external JSONL corpus to pre-load into SAM at startup. Additional corpora can be added at runtime via POST /add_external_corpus.",
)
parser.add_argument(
"--speculative-ngram-external-sam-budget",
type=int,
default=ServerArgs.speculative_ngram_external_sam_budget,
help="Number of draft nodes reserved for the external SAM subtree in ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-external-corpus-max-tokens",
type=int,
default=ServerArgs.speculative_ngram_external_corpus_max_tokens,
help="Fail startup if the tokenized external ngram corpus exceeds this many tokens. Tune this based on your CPU memory budget.",
)
parser.add_argument(
"--speculative-adaptive",
action="store_true",
help="Enable adaptive speculative decoding that dynamically adjusts num_steps based on acceptance rate.",
default=ServerArgs.speculative_adaptive,
)
parser.add_argument(
"--speculative-adaptive-config",
type=str,
help="Path to a JSON config file for adaptive speculative decoding tuning knobs ",
default=ServerArgs.speculative_adaptive_config,
)
# Multi-layer Eagle speculative decoding
parser.add_argument(
"--enable-multi-layer-eagle",
action="store_true",
help="Enable multi-layer Eagle speculative decoding.",
)
# Expert parallelism
parser.add_argument(
"--expert-parallel-size",
"--ep-size",
"--ep",
type=int,
default=ServerArgs.ep_size,
help="The expert parallelism size.",
)
parser.add_argument(
"--moe-a2a-backend",
type=str,
choices=MOE_A2A_BACKEND_CHOICES,
default=ServerArgs.moe_a2a_backend,
help="Choose the backend for MoE A2A.",
)
parser.add_argument(
"--moe-runner-backend",
type=str,
choices=MOE_RUNNER_BACKEND_CHOICES,
default=ServerArgs.moe_runner_backend,
help="Choose the runner backend for MoE.",
)
parser.add_argument(
"--record-nolora-graph",
action=argparse.BooleanOptionalAction,
default=ServerArgs.record_nolora_graph,
help="Capture a second set of CUDA graphs without LoRA hooks. "
"Batches without active adapters replay the faster nolora graph. "
"Enabled by default.",
)
parser.add_argument(
"--flashinfer-mxfp4-moe-precision",
type=str,
choices=["default", "bf16"],
default=ServerArgs.flashinfer_mxfp4_moe_precision,
help="Choose the computation precision of flashinfer mxfp4 moe",
)
parser.add_argument(
"--enable-flashinfer-allreduce-fusion",
action="store_true",
help="Enable FlashInfer allreduce fusion with Residual RMSNorm.",
)
parser.add_argument(
"--enforce-disable-flashinfer-allreduce-fusion",
action="store_true",
help="Enforce disable FlashInfer allreduce fusion.",
)
parser.add_argument(
"--enable-aiter-allreduce-fusion",
action="store_true",
help="Enable Aiter AllReduce Fusion.",
)
parser.add_argument(
"--deepep-mode",
type=str,
choices=["normal", "low_latency", "auto"],
default="auto",
help="Select the mode when enable DeepEP or MoriEP 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(
"--eplb-min-rebalancing-utilization-threshold",
type=float,
default=ServerArgs.eplb_min_rebalancing_utilization_threshold,
help="Minimum threshold for GPU average utilization to trigger EPLB rebalancing. Must be in the range [0.0, 1.0].",
)
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.",
)
parser.add_argument(
"--elastic-ep-backend",
type=str,
default=ServerArgs.elastic_ep_backend,
choices=["none", "mooncake", "nixl"],
help="Specify the collective communication backend for elastic EP. Supports 'mooncake' and 'nixl'.",
)
parser.add_argument(
"--enable-elastic-expert-backup",
action="store_true",
default=ServerArgs.enable_elastic_expert_backup,
help="Enable elastic expert backup feature.",
)
parser.add_argument(
"--mooncake-ib-device",
type=str,
default=ServerArgs.mooncake_ib_device,
help="The InfiniBand devices for Mooncake Backend transfer, accepts multiple comma-separated devices "
"(e.g., --mooncake-ib-device mlx5_0,mlx5_1). "
"Default is None, which triggers automatic device detection when Mooncake Backend is enabled.",
)
parser.add_argument(
"--elastic-ep-rejoin",
action="store_true",
default=ServerArgs.elastic_ep_rejoin,
help="Indicates that this process is a relaunched elastic EP rank that should rejoin an existing process group.",
)
# Mamba Cache
parser.add_argument(
"--max-mamba-cache-size",
type=int,
default=ServerArgs.max_mamba_cache_size,
help="The maximum size of the mamba cache.",
)
parser.add_argument(
"--mamba-ssm-dtype",
type=str,
default=None,
choices=["float32", "bfloat16", "float16"],
help="The data type of the SSM states in mamba cache. "
"If not set, will be read from model config (mamba_ssm_dtype).",
)
parser.add_argument(
"--mamba-full-memory-ratio",
type=float,
default=ServerArgs.mamba_full_memory_ratio,
help="The ratio of mamba state memory to full kv cache memory.",
)
parser.add_argument(
"--mamba-scheduler-strategy",
type=str,
choices=MAMBA_SCHEDULER_STRATEGY_CHOICES,
default=ServerArgs.mamba_scheduler_strategy,
help="The strategy to use for mamba radix cache.",
)
parser.add_argument(
"--mamba-track-interval",
type=int,
default=ServerArgs.mamba_track_interval,
help="The interval to track the mamba state during decode.",
)
parser.add_argument(
"--mamba-backend",
type=str,
choices=MAMBA_BACKEND_CHOICES,
default=ServerArgs.mamba_backend,
help="Choose the kernel backend for Mamba SSM operations. Default is 'triton'. "
"Options: 'triton' (default), 'flashinfer' (requires FlashInfer with Mamba support).",
)
parser.add_argument(
"--linear-attn-backend",
type=str,
choices=LINEAR_ATTN_KERNEL_BACKEND_CHOICES,
default=ServerArgs.linear_attn_backend,
help="The default kernel backend for linear attention (GDN/KDA). "
"Can be overridden per-mode by --linear-attn-decode-backend "
"and --linear-attn-prefill-backend.",
)
parser.add_argument(
"--linear-attn-decode-backend",
type=str,
choices=LINEAR_ATTN_KERNEL_BACKEND_CHOICES,
default=ServerArgs.linear_attn_decode_backend,
help="Override the kernel backend for linear attention decode. "
"If not set, uses --linear-attn-backend.",
)
parser.add_argument(
"--linear-attn-prefill-backend",
type=str,
choices=LINEAR_ATTN_KERNEL_BACKEND_CHOICES,
default=ServerArgs.linear_attn_prefill_backend,
help="Override the kernel backend for linear attention prefill/extend. "
"If not set, uses --linear-attn-backend.",
)
# Hierarchical cache
parser.add_argument(
"--enable-hierarchical-cache",
action="store_true",
help="Enable hierarchical cache",
)
parser.add_argument(
"--hicache-ratio",
type=float,
default=ServerArgs.hicache_ratio,
help="The ratio of the size of host KV cache memory pool to the size of device pool.",
)
parser.add_argument(
"--hicache-size",
type=int,
default=ServerArgs.hicache_size,
help="The size of host KV cache memory pool in gigabytes, which will override the hicache_ratio if set.",
)
parser.add_argument(
"--hicache-write-policy",
type=str,
choices=["write_back", "write_through", "write_through_selective"],
default=ServerArgs.hicache_write_policy,
help="The write policy of hierarchical cache.",
)
parser.add_argument(
"--hicache-io-backend",
type=str,
choices=["direct", "kernel", "kernel_ascend"],
default=ServerArgs.hicache_io_backend,
help="The IO backend for KV cache transfer between CPU and GPU",
)
parser.add_argument(
"--hicache-mem-layout",
type=str,
choices=[
"layer_first",
"page_first",
"page_first_direct",
"page_first_kv_split",
"page_head",
],
default=ServerArgs.hicache_mem_layout,
help="The layout of host memory pool for hierarchical cache.",
)
parser.add_argument(
"--hicache-storage-backend",
type=str,
choices=[
"file",
"mooncake",
"hf3fs",
"nixl",
"aibrix",
"dynamic",
"eic",
"simm",
],
default=ServerArgs.hicache_storage_backend,
help="The storage backend for hierarchical KV cache. "
"Built-in backends: file, mooncake, hf3fs, nixl, aibrix. "
"For dynamic backend, use --hicache-storage-backend-extra-config to specify: "
"backend_name (custom name), module_path (Python module path), class_name (backend class name).",
)
parser.add_argument(
"--hicache-storage-prefetch-policy",
type=str,
choices=["best_effort", "wait_complete", "timeout"],
default=ServerArgs.hicache_storage_prefetch_policy,
help="Control when prefetching from the storage backend should stop.",
)
parser.add_argument(
"--hicache-storage-backend-extra-config",
type=str,
default=ServerArgs.hicache_storage_backend_extra_config,
help="A dictionary in JSON string format, or a string starting with a leading '@' and a config file in JSON/YAML/TOML format, containing extra configuration for the storage backend.",
)
# Hierarchical sparse attention
parser.add_argument(
"--enable-hisparse",
action="store_true",
help="Enable hierarchical sparse attention",
)
parser.add_argument(
"--hisparse-config",
type=str,
default=ServerArgs.hisparse_config,
help="A dictionary in JSON string format for hierarchical sparse attention configuration. "
'Example: \'{"top_k": 2048, "device_buffer_size": 4096}\'',
)
# LMCache
parser.add_argument(
"--enable-lmcache",
action="store_true",
help="Using LMCache as an alternative hierarchical cache solution",
)
# Ktransformer server args
parser.add_argument(
"--kt-weight-path",
type=str,
help="[ktransformers parameter] The path of the quantized expert weights for amx kernel. A local folder.",
)
parser.add_argument(
"--kt-method",
type=str,
default="AMXINT4",
help="[ktransformers parameter] Quantization formats for CPU execution.",
)
parser.add_argument(
"--kt-cpuinfer",
type=int,
help="[ktransformers parameter] The number of CPUInfer threads.",
)
parser.add_argument(
"--kt-threadpool-count",
type=int,
default=2,
help="[ktransformers parameter] One-to-one with the number of NUMA nodes (one thread pool per NUMA).",
)
parser.add_argument(
"--kt-num-gpu-experts",
type=int,
help="[ktransformers parameter] The number of GPU experts.",
)
parser.add_argument(
"--kt-max-deferred-experts-per-token",
type=int,
default=ServerArgs.kt_max_deferred_experts_per_token,
help="[ktransformers parameter] Maximum number of experts deferred to CPU per token. All MoE layers except the final one use this value; the final layer always uses 0.",
)
# Diffusion LLM
parser.add_argument(
"--dllm-algorithm",
type=str,
default=ServerArgs.dllm_algorithm,
help="The diffusion LLM algorithm, such as LowConfidence.",
)
parser.add_argument(
"--dllm-algorithm-config",
type=str,
default=ServerArgs.dllm_algorithm_config,
help="The diffusion LLM algorithm configurations. Must be a YAML file.",
)
# Offloading
parser.add_argument(
"--cpu-offload-gb",
type=int,
default=ServerArgs.cpu_offload_gb,
help="How many GBs of RAM to reserve for CPU offloading.",
)
parser.add_argument(
"--offload-group-size",
type=int,
default=ServerArgs.offload_group_size,
help="Number of layers per group in offloading.",
)
parser.add_argument(
"--offload-num-in-group",
type=int,
default=ServerArgs.offload_num_in_group,
help="Number of layers to be offloaded within a group.",
)
parser.add_argument(
"--offload-prefetch-step",
type=int,
default=ServerArgs.offload_prefetch_step,
help="Steps to prefetch in offloading.",
)
parser.add_argument(
"--offload-mode",
type=str,
default=ServerArgs.offload_mode,
help="Mode of offloading.",
)
# Args for multi-item-scoring
parser.add_argument(
"--enable-mis",
action="store_true",
default=ServerArgs.enable_mis,
help="Enable Multi-Item Scoring optimization. Combines query and multiple items "
"into a single sequence for efficient batch processing. "
"Requires --attention-backend flashinfer; auto-disables CUDA graph, "
"radix cache, and chunked prefill.",
)
# Optimization/debug options
parser.add_argument(
"--disable-radix-cache",
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",
help="Disable cuda graph.",
)
parser.add_argument(
"--disable-cuda-graph-padding",
action="store_true",
help="Disable cuda graph when padding is needed. Still uses cuda graph when padding is not needed.",
)
parser.add_argument(
"--enable-breakable-cuda-graph",
action="store_true",
help="Use breakable CUDA graph for piecewise capture instead of torch.compile-based splitting.",
)
parser.add_argument(
"--enable-profile-cuda-graph",
action="store_true",
help="Enable profiling of cuda graph capture.",
)
parser.add_argument(
"--enable-cudagraph-gc",
action="store_true",
help="Enable garbage collection during CUDA graph capture. If disabled (default), GC is frozen during capture to speed up the process.",
)
parser.add_argument(
"--debug-cuda-graph",
action="store_true",
help="Enable debug/eager mode for CUDA graph using breakable CUDA graph. "
"When enabled, graph breaks are inserted so every operation runs eagerly "
"while still going through the CUDA graph capture / replay path. "
"Useful for debugging CUDA graph capture / replay issues.",
)
parser.add_argument(
"--enable-layerwise-nvtx-marker",
action="store_true",
help="Enable layerwise NVTX profiling annotations for the model.",
)
parser.add_argument(
"--enable-nccl-nvls",
action="store_true",
help="Enable NCCL NVLS for prefill heavy requests when available.",
)
parser.add_argument(
"--enable-symm-mem",
action="store_true",
help="Enable NCCL symmetric memory for fast collectives.",
)
parser.add_argument(
"--disable-flashinfer-cutlass-moe-fp4-allgather",
action="store_true",
help="Disables quantize before all-gather for flashinfer cutlass moe.",
)
parser.add_argument(
"--enable-tokenizer-batch-encode",
action="store_true",
help="Enable batch tokenization for improved performance when processing multiple text inputs. Do not use with image inputs, pre-tokenized input_ids, or input_embeds.",
)
parser.add_argument(
"--disable-tokenizer-batch-decode",
action="store_true",
help="Disable batch decoding when decoding multiple completions.",
)
parser.add_argument(
"--disable-outlines-disk-cache",
action="store_true",
help="Disable disk cache of outlines to avoid possible crashes related to file system or high concurrency.",
)
parser.add_argument(
"--disable-custom-all-reduce",
action="store_true",
help="Disable the custom all-reduce kernel and fall back to NCCL.",
)
parser.add_argument(
"--enable-mscclpp",
action="store_true",
help="Enable using mscclpp for small messages for all-reduce kernel and fall back to NCCL.",
)
parser.add_argument(
"--enable-torch-symm-mem",
action="store_true",
help="Enable using torch symm mem for all-reduce kernel and fall back to NCCL. Only supports CUDA device SM90 and above. SM90 supports world size 4, 6, 8. SM100 supports world size 6, 8.",
)
parser.add_argument(
"--pre-warm-nccl",
action="store_true",
help="Pre-warm NCCL/RCCL communicators during startup to reduce P99 TTFT cold-start latency. Default: enabled for AMD/HIP (RCCL), disabled for NVIDIA/CUDA (NCCL).",
)
parser.add_argument(
"--disable-overlap-schedule",
action="store_true",
help="Disable the overlap scheduler, which overlaps the CPU scheduler with GPU model worker.",
)
parser.add_argument(
"--enable-mixed-chunk",
action="store_true",
help="Enabling mixing prefill and decode in a batch when using chunked prefill.",
)
parser.add_argument(
"--enable-dp-attention",
action="store_true",
help="Enabling data parallelism for attention and tensor parallelism for FFN. The dp size should be equal to the tp size. Currently DeepSeek-V2 and Qwen 2/3 MoE models are supported.",
)
parser.add_argument(
"--enable-dp-attention-local-control-broadcast",
action="store_true",
help="With DP-attention, send control messages to every DP group leader "
"and broadcast within attn_tp_group instead of the full tp_group. "
"Eliminates a costly all-ranks gloo sync on every scheduler iteration.",
)
parser.add_argument(
"--enable-dp-lm-head",
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-two-batch-overlap",
action="store_true",
help="Enabling two micro batches to overlap.",
)
parser.add_argument(
"--enable-single-batch-overlap",
action="store_true",
help="Let computation and communication overlap within one micro batch.",
)
parser.add_argument(
"--tbo-token-distribution-threshold",
type=float,
default=ServerArgs.tbo_token_distribution_threshold,
help="The threshold of token distribution between two batches in micro-batch-overlap, determines whether to two-batch-overlap or two-chunk-overlap. Set to 0 denote disable two-chunk-overlap.",
)
parser.add_argument(
"--enable-torch-compile",
action="store_true",
help="Optimize the model with torch.compile. Experimental feature.",
)
parser.add_argument(
"--enable-torch-compile-debug-mode",
action="store_true",
help="Enable debug mode for torch compile",
)
parser.add_argument(
"--disable-piecewise-cuda-graph",
action="store_true",
help="Disable piecewise cuda graph for extend/prefill.",
)
parser.add_argument(
"--enable-piecewise-cuda-graph",
action=DeprecatedAction,
help="Deprecated: Piecewise cuda graph is enabled by default. Use --enforce-piecewise-cuda-graph to skip auto-disable conditions.",
)
parser.add_argument(
"--enforce-piecewise-cuda-graph",
action="store_true",
help="Enforce piecewise cuda graph, skipping all auto-disable conditions. Used for testing.",
)
parser.add_argument(
"--piecewise-cuda-graph-tokens",
type=int,
nargs="+",
help="Set the list of token lengths for piecewise cuda graph capture.",
)
parser.add_argument(
"--piecewise-cuda-graph-compiler",
type=str,
default=ServerArgs.piecewise_cuda_graph_compiler,
help="Set the compiler for piecewise cuda graph. Choices are: eager, inductor.",
choices=["eager", "inductor"],
)
parser.add_argument(
"--torch-compile-max-bs",
type=int,
default=ServerArgs.torch_compile_max_bs,
help="Set the maximum batch size when using torch compile.",
)
parser.add_argument(
"--piecewise-cuda-graph-max-tokens",
type=int,
default=ServerArgs.piecewise_cuda_graph_max_tokens,
help="Set the maximum tokens when using piecewise cuda graph.",
)
parser.add_argument(
"--torchao-config",
type=str,
default=ServerArgs.torchao_config,
help="Optimize the model with torchao. Experimental feature. Current choices are: int8dq, int8wo, int4wo-<group_size>, fp8wo, fp8dq-per_tensor, fp8dq-per_row",
)
parser.add_argument(
"--enable-nan-detection",
action="store_true",
help="[Deprecated] Use SGLANG_SPEC_NAN_DETECTION=1 and SGLANG_SPEC_OOB_DETECTION=1 instead.",
)
parser.add_argument(
"--enable-p2p-check",
action="store_true",
help="Enable P2P check for GPU access, otherwise the p2p access is allowed by default.",
)
parser.add_argument(
"--triton-attention-reduce-in-fp32",
action="store_true",
help="Cast the intermediate attention results to fp32 to avoid possible crashes related to fp16."
"This only affects Triton attention kernels.",
)
parser.add_argument(
"--triton-attention-num-kv-splits",
type=int,
default=ServerArgs.triton_attention_num_kv_splits,
help="The number of KV splits in flash decoding Triton kernel. Larger value is better in longer context scenarios. The default value is 8.",
)
parser.add_argument(
"--triton-attention-split-tile-size",
type=int,
default=ServerArgs.triton_attention_split_tile_size,
help="The size of split KV tile in flash decoding Triton kernel. Used for deterministic inference.",
)
parser.add_argument(
"--num-continuous-decode-steps",
type=int,
default=ServerArgs.num_continuous_decode_steps,
help="Run multiple continuous decoding steps to reduce scheduling overhead. "
"This can potentially increase throughput but may also increase time-to-first-token latency. "
"The default value is 1, meaning only run one decoding step at a time.",
)
parser.add_argument(
"--delete-ckpt-after-loading",
action="store_true",
help="Delete the model checkpoint after loading the model.",
)
parser.add_argument(
"--enable-memory-saver",
action="store_true",
help="Allow saving memory using release_memory_occupation and resume_memory_occupation",
)
parser.add_argument(
"--enable-weights-cpu-backup",
action="store_true",
help="Save model weights (both main model and draft model, if any) to CPU memory during release_weights_occupation and resume_weights_occupation",
)
parser.add_argument(
"--enable-draft-weights-cpu-backup",
action="store_true",
help="Save draft model weights to CPU memory during release_weights_occupation and resume_weights_occupation",
)
parser.add_argument(
"--allow-auto-truncate",
action="store_true",
help="Allow automatically truncating requests that exceed the maximum input length instead of returning an error.",
)
parser.add_argument(
"--enable-custom-logit-processor",
action="store_true",
help="Enable users to pass custom logit processors to the server (disabled by default for security)",
)
parser.add_argument(
"--flashinfer-mla-disable-ragged",
action="store_true",
help="Not using ragged prefill wrapper when running flashinfer mla",
)
parser.add_argument(
"--disable-shared-experts-fusion",
action="store_true",
help="Disable shared experts fusion optimization for deepseek v3/r1.",
)
parser.add_argument(
"--enforce-shared-experts-fusion",
action="store_true",
help="Enforce shared experts fusion even when it would normally be disabled (e.g. under DeepEP). "
"Mutually exclusive with --disable-shared-experts-fusion.",
)
parser.add_argument(
"--disable-chunked-prefix-cache",
action="store_true",
help="Disable chunked prefix cache feature for deepseek, which should save overhead for short sequences.",
)
parser.add_argument(
"--disable-fast-image-processor",
action="store_true",
help="Adopt base image processor instead of fast image processor.",
)
parser.add_argument(
"--keep-mm-feature-on-device",
action="store_true",
help="Keep multimodal feature tensors on device after processing to save D2H copy.",
)
parser.add_argument(
"--enable-return-hidden-states",
action="store_true",
help="Enable returning hidden states with responses.",
)
parser.add_argument(
"--enable-return-routed-experts",
action="store_true",
help="Enable returning routed experts of each layer with responses.",
)
parser.add_argument(
"--scheduler-recv-interval",
type=int,
default=ServerArgs.scheduler_recv_interval,
help="The interval to poll requests in scheduler. Can be set to >1 to reduce the overhead of this.",
)
parser.add_argument(
"--numa-node",
type=int,
nargs="+",
help="Sets the numa node for the subprocesses. i-th element corresponds to i-th subprocess. If unset, will be automatically detected on NUMA systems.",
)
parser.add_argument(
"--enable-deterministic-inference",
action="store_true",
help="Enable deterministic inference mode with batch invariant ops.",
)
parser.add_argument(
"--rl-on-policy-target",
type=str,
default=ServerArgs.rl_on_policy_target,
choices=RL_ON_POLICY_TARGET_CHOICES,
help="The training system that SGLang needs to match for true on-policy.",
)
parser.add_argument(
"--enable-attn-tp-input-scattered",
action="store_true",
help="Allow input of attention to be scattered when only using tensor parallelism, to reduce the computational load of operations such as qkv latent.",
)
parser.add_argument(
"--enable-nsa-prefill-context-parallel",
action="store_true",
help="Enable context parallelism used in the long sequence prefill phase of DeepSeek v3.2.",
)
parser.add_argument(
"--nsa-prefill-cp-mode",
type=str,
default=ServerArgs.nsa_prefill_cp_mode,
choices=NSA_PREFILL_CP_SPLIT_CHOICES,
help="Token splitting mode for the prefill phase of DeepSeek v3.2 under context parallelism. Optional values: 'round-robin-split'(default), 'in-seq-split' "
"'round-robin-split' distributes tokens across ranks based on token_idx %% cp_size. It supports multi-batch prefill, fused MoE, and FP8 KV cache.",
)
parser.add_argument(
"--enable-prefill-context-parallel",
action="store_true",
help="Enable context parallelism used in the prefill phase",
)
parser.add_argument(
"--prefill-cp-mode",
type=str,
default=ServerArgs.prefill_cp_mode,
choices=PREFILL_CP_SPLIT_CHOICES,
help="Token splitting mode for the prefill phase under context parallelism. Optional values: 'in-seq-split' (default)",
)
parser.add_argument(
"--enable-fused-qk-norm-rope",
action="store_true",
help="Enable fused qk normalization and rope rotary embedding.",
)
parser.add_argument(
"--enable-precise-embedding-interpolation",
action="store_true",
help="Enable corner alignment for resize of embeddings grid to ensure more accurate(but slower) evaluation of interpolated embedding values.",
)
parser.add_argument(
"--enable-fused-moe-sum-all-reduce",
action="store_true",
help="Enable fused moe triton and sum all reduce.",
)
parser.add_argument(
"--gc-threshold",
type=int,
nargs="+",
help="Set the garbage collection thresholds (the collection frequency). Accepts 1 to 3 integers.",
)
# Dynamic batch tokenizer
parser.add_argument(
"--enable-dynamic-batch-tokenizer",
action="store_true",
help="Enable async dynamic batch tokenizer for improved performance when multiple requests arrive concurrently.",
)
parser.add_argument(
"--dynamic-batch-tokenizer-batch-size",
type=int,
default=ServerArgs.dynamic_batch_tokenizer_batch_size,
help="[Only used if --enable-dynamic-batch-tokenizer is set] Maximum batch size for dynamic batch tokenizer.",
)
parser.add_argument(
"--dynamic-batch-tokenizer-batch-timeout",
type=float,
default=ServerArgs.dynamic_batch_tokenizer_batch_timeout,
help="[Only used if --enable-dynamic-batch-tokenizer is set] Timeout in seconds for batching tokenization requests.",
)
# Debug tensor dumps
parser.add_argument(
"--debug-tensor-dump-output-folder",
type=str,
default=ServerArgs.debug_tensor_dump_output_folder,
help=(
"The output folder for dumping tensors. "
"In Eagle mode, tensor outputs from draft and target models "
"are stored in separate subdirectories ('draft' and 'target')."
),
)
parser.add_argument(
"--debug-tensor-dump-layers",
type=int,
nargs="+",
help="The layer ids to dump. Dump all layers if not specified.",
)
parser.add_argument(
"--debug-tensor-dump-input-file",
type=str,
default=ServerArgs.debug_tensor_dump_input_file,
help="The input filename for dumping tensors",
)
parser.add_argument(
"--debug-tensor-dump-inject",
type=str,
default=ServerArgs.debug_tensor_dump_inject,
help="Inject the outputs from jax as the input of every layer.",
)
# PD disaggregation
parser.add_argument(
"--disaggregation-mode",
type=str,
default=ServerArgs.disaggregation_mode,
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-transfer-backend",
type=str,
default=ServerArgs.disaggregation_transfer_backend,
choices=DISAGG_TRANSFER_BACKEND_CHOICES,
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,
default=ServerArgs.disaggregation_ib_device,
help="The InfiniBand devices for disaggregation transfer, accepts single device (e.g., --disaggregation-ib-device mlx5_0) "
"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(
"--disaggregation-decode-enable-offload-kvcache",
action="store_true",
help="Enable async KV cache offloading on decode server (PD mode).",
)
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(
"--disaggregation-decode-polling-interval",
type=int,
default=ServerArgs.disaggregation_decode_polling_interval,
help="The interval to poll requests in decode server. Can be set to >1 to reduce the overhead of this.",
)
# Encode prefill disaggregation
parser.add_argument(
"--encoder-only",
action="store_true",
help="For MLLM with an encoder, launch an encoder-only server",
)
parser.add_argument(
"--language-only",
action="store_true",
help="For VLM, load weights for the language model only.",
)
parser.add_argument(
"--encoder-transfer-backend",
type=str,
default=ServerArgs.encoder_transfer_backend,
choices=ENCODER_TRANSFER_BACKEND_CHOICES,
help="The backend for encoder disaggregation transfer. Default is zmq_to_scheduler.",
)
parser.add_argument(
"--encoder-urls",
nargs="+",
type=str,
default=[],
help="List of encoder server urls.",
)
parser.add_argument(
"--enable-adaptive-dispatch-to-encoder",
default=ServerArgs.enable_adaptive_dispatch_to_encoder,
action="store_true",
help="When enabled, adaptively dispatch: multi-image requests go to encoder in language_only epd mode, single-image requests are processed locally.",
)
# Custom weight loader
parser.add_argument(
"--custom-weight-loader",
type=str,
nargs="*",
default=None,
help="The custom dataloader which used to update the model. Should be set with a valid import path, such as my_package.weight_load_func",
)
parser.add_argument(
"--weight-loader-disable-mmap",
action="store_true",
help="Disable mmap while loading weight using safetensors.",
)
parser.add_argument(
"--weight-loader-prefetch-checkpoints",
action="store_true",
help="Prefetch checkpoint files into OS page cache before loading. "
"Each rank prefetches a fraction of the shards, reducing total "
"network I/O on shared filesystems (NFS/Lustre) from N*checkpoint "
"to 1*checkpoint. Recommended for models on network storage.",
)
parser.add_argument(
"--weight-loader-prefetch-num-threads",
type=int,
default=ServerArgs.weight_loader_prefetch_num_threads,
help="Number of threads per rank for checkpoint prefetching (default: 4).",
)
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(
"--remote-instance-weight-loader-backend",
type=str,
choices=["transfer_engine", "nccl", "modelexpress"],
default=ServerArgs.remote_instance_weight_loader_backend,
help="The backend for loading weights from remote instance. Can be 'transfer_engine', 'nccl', or 'modelexpress'. Default is 'nccl'.",
)
parser.add_argument(
"--remote-instance-weight-loader-start-seed-via-transfer-engine",
action="store_true",
help="Start seed server via transfer engine backend for remote instance weight loader.",
)
parser.add_argument(
"--engine-info-bootstrap-port",
type=int,
default=ServerArgs.engine_info_bootstrap_port,
help="Port for the engine info bootstrap server. Default is 6789. "
"Must be set explicitly when running multiple instances on the same node.",
)
parser.add_argument(
"--modelexpress-config",
type=str,
default=ServerArgs.modelexpress_config,
help='JSON config for ModelExpress P2P weight loading. Keys: "url" (required, gRPC host:port), "model_name" (optional, defaults to --model-path), "source" (optional bool, true for seed mode). Example: \'{"url": "localhost:8001", "model_name": "my-model", "source": true}\'',
)
# For PD-Multiplexing
parser.add_argument(
"--enable-pdmux",
action="store_true",
help="Enable PD-Multiplexing, PD running on greenctx stream.",
)
parser.add_argument(
"--pdmux-config-path",
type=str,
default=None,
help="The path of the PD-Multiplexing config file.",
)
parser.add_argument(
"--sm-group-num",
type=int,
default=ServerArgs.sm_group_num,
help="Number of sm partition groups.",
)
# Configuration file support
parser.add_argument(
"--config",
type=str,
help="Read CLI options from a config file. Must be a YAML file with configuration options.",
)
# For Multi-Modal
parser.add_argument(
"--enable-broadcast-mm-inputs-process",
action="store_true",
default=ServerArgs.enable_broadcast_mm_inputs_process,
help="Enable broadcast mm-inputs process in scheduler.",
)
parser.add_argument(
"--mm-process-config",
type=json.loads,
default=ServerArgs.mm_process_config,
help="Multimodal preprocessing config, a json config contains keys: `image`, `video`, `audio`",
)
parser.add_argument(
"--mm-enable-dp-encoder",
action="store_true",
default=ServerArgs.mm_enable_dp_encoder,
help="Enabling data parallelism for mm encoder. The dp size will be set to the tp size automatically.",
)
parser.add_argument(
"--limit-mm-data-per-request",
type=json.loads,
default=ServerArgs.limit_mm_data_per_request,
help="Limit the number of multimodal inputs per request. "
'e.g. \'{"image": 1, "video": 1, "audio": 1}\'',
)
# For checkpoint decryption
parser.add_argument(
"--decrypted-config-file",
type=str,
default=ServerArgs.decrypted_config_file,
help="The path of the decrypted config file.",
)
parser.add_argument(
"--decrypted-draft-config-file",
type=str,
default=ServerArgs.decrypted_draft_config_file,
help="The path of the decrypted draft config file.",
)
parser.add_argument(
"--enable-prefix-mm-cache",
action="store_true",
default=ServerArgs.enable_prefix_mm_cache,
help="Enable prefix multimodal cache. Currently only supports mm-only.",
)
parser.add_argument(
"--enable-mm-global-cache",
action="store_true",
default=ServerArgs.enable_mm_global_cache,
help="Enable global multimodal embedding cache to skip redundant ViT inference.",
)
# For registering hooks
parser.add_argument(
"--forward-hooks",
type=json_list_type,
default=ServerArgs.forward_hooks,
help="JSON-formatted forward hook specifications to attach to the model.",
)
# For msProbe
parser.add_argument(
"--msprobe-dump-config",
type=str,
default=ServerArgs.msprobe_dump_config,
help="The path of the JSON configuration file for msProbe. If specified, enables msProbe dump.",
)
@classmethod
def from_cli_args(cls, args: argparse.Namespace):
args.tp_size = args.tensor_parallel_size
args.pp_size = args.pipeline_parallel_size
args.attn_cp_size = args.attention_context_parallel_size
args.moe_dp_size = args.moe_data_parallel_size
args.dp_size = args.data_parallel_size
args.ep_size = args.expert_parallel_size
attrs = [attr.name for attr in dataclasses.fields(cls)]
return cls(**{attr: getattr(args, attr) for attr in attrs})
def url(self, port: Optional[int] = None):
scheme = "https" if self.ssl_certfile else "http"
# When binding to all interfaces, use loopback for internal requests.
host = self.host
if not host or host == "0.0.0.0":
host = "127.0.0.1"
elif host == "::":
host = "::1"
return NetworkAddress(host, port if port is not None else self.port).to_url(
scheme
)
@property
def engine_info_bootstrap_url(self):
return self.url(port=self.engine_info_bootstrap_port)
def ssl_verify(self):
"""Return the value for the requests library's ``verify=`` parameter.
When SSL is configured:
- If a CA certificate file is provided, return its path so requests
validates the server certificate against that CA.
- Otherwise, return False to disable certificate verification
(suitable for self-signed certificates in development/testing).
A warning is logged once when this happens.
When SSL is not configured, return True to use the system's default
CA bundle.
"""
if self.ssl_ca_certs:
return self.ssl_ca_certs
if self.ssl_certfile:
if not getattr(self, "_ssl_verify_warned", False):
logger.warning(
"SSL is enabled but --ssl-ca-certs was not provided. "
"Certificate verification is DISABLED for internal "
"health checks. For production deployments, provide "
"--ssl-ca-certs or use CA-signed certificates."
)
self._ssl_verify_warned = True
return False
return True
def get_model_config(self):
# Lazy init to avoid circular import
from sglang.srt.configs.model_config import ModelConfig
if hasattr(self, "model_config"):
return self.model_config
self.model_config = ModelConfig.from_server_args(self)
return self.model_config
def get_attention_backends(self):
prefill_attention_backend_str = (
self.prefill_attention_backend
if self.prefill_attention_backend
else self.attention_backend
)
decode_attention_backend_str = (
self.decode_attention_backend
if self.decode_attention_backend
else self.attention_backend
)
return prefill_attention_backend_str, decode_attention_backend_str
def use_mla_backend(self):
from sglang.srt.configs.model_config import AttentionArch
model_config = self.get_model_config()
return model_config.attention_arch == AttentionArch.MLA
def is_attention_backend_not_set(self):
return (
self.attention_backend is None
and self.prefill_attention_backend is None
and self.decode_attention_backend is None
)
def enable_mamba_extra_buffer(self) -> bool:
return self.mamba_scheduler_strategy == "extra_buffer"
@property
def mamba_cache_chunk_size(self) -> int:
# For mamba cache with extra buffer, the chunk size is the max of FLA_CHUNK_SIZE and page_size.
# It is used to determine the caching point in a sequence during prefill.
return max(FLA_CHUNK_SIZE, self.page_size)
def check_server_args(self):
# Check parallel size constraints
assert (
self.tp_size * self.pp_size
) % self.nnodes == 0, "tp_size must be divisible by number of nodes"
assert (
self.pp_max_micro_batch_size is None or self.pp_max_micro_batch_size >= 1
), (
"pp_max_micro_batch_size must be a positive integer or None (for auto-compute). "
f"Got: {self.pp_max_micro_batch_size}"
)
if self.pp_size > 1:
assert (
self.disable_overlap_schedule and self.speculative_algorithm is None
), "Pipeline parallelism is not compatible with overlap schedule, speculative decoding"
assert not (
self.dp_size > 1 and self.nnodes != 1 and not self.enable_dp_attention
), "multi-node data parallel is not supported unless dp attention!"
assert self.base_gpu_id >= 0, "base_gpu_id must be non-negative"
assert self.gpu_id_step >= 1, "gpu_id_step must be positive"
assert self.moe_dense_tp_size in {
1,
None,
}, "moe_dense_tp_size only support 1 and None currently"
# Check served model name to not have colon as it is reserved for LoRA adapter syntax
if not is_runai_obj_uri(self.served_model_name):
assert ":" not in self.served_model_name, (
"served_model_name cannot contain a colon (':') character. "
"The colon is reserved for the 'model:adapter' syntax used in LoRA adapter specification. "
f"Invalid value: '{self.served_model_name}'"
)
# Check LoRA
self.check_lora_server_args()
# Check speculative decoding
if self.speculative_algorithm is not None:
assert (
not self.enable_mixed_chunk
), "enable_mixed_chunk is required for speculative decoding"
# Check chunked prefill
# 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 % self.page_size == 0
), "chunked_prefill_size must be divisible by page_size"
# Check pdmux
if self.enable_pdmux:
assert (
self.pp_size == 1
), "PD-Multiplexing is only supported with pipeline parallelism disabled (pp_size=1)."
assert (
self.chunked_prefill_size == -1
), "PD-Multiplexing is not compatible with chunked prefill."
assert (
self.disaggregation_mode == "null"
), "PD-Multiplexing is not compatible with disaggregation mode."
assert (
self.disable_overlap_schedule
), "PD-Multiplexing is not compatible with overlap schedule."
# NOTE: CUDA Green Context may encounter potential issues with CudaGraph on torch 2.7.x – 2.8.x, leading to performance degradation.
import torch
if torch_release >= (2, 7):
logger.warning(
"WARNING: PD-Multiplexing may experience performance degradation with torch versions > 2.6.x.\n"
f" Current torch version is {torch.__version__}.\n"
" Please manually install torch 2.6.x."
)
assert self.tokenizer_worker_num > 0, "Tokenizer worker num must >= 1"
self.validate_buckets_rule(
"--prompt-tokens-buckets", self.prompt_tokens_buckets
)
self.validate_buckets_rule(
"--generation-tokens-buckets", self.generation_tokens_buckets
)
# Check scheduling policy
if self.enable_priority_scheduling:
assert self.schedule_policy in [
"fcfs",
"lof",
], f"To use priority scheduling, schedule_policy must be 'fcfs' or 'lof'. '{self.schedule_policy}' is not supported."
if self.default_priority_value is None:
logger.warning(
"--default-priority-value is not set while --enable-priority-scheduling is enabled. "
"Requests without explicit priority will have priority=None, "
"resulting in priority='None' string labels in Prometheus metrics."
)
else:
if self.disable_priority_preemption:
logger.warning(
"--disable-priority-preemption has no effect without --enable-priority-scheduling"
)
if self.default_priority_value is not None:
logger.warning(
"--default-priority-value has no effect without --enable-priority-scheduling"
)
# Check hisparse
if self.enable_hisparse:
from sglang.srt.configs.model_config import is_deepseek_nsa
hf_config = self.get_model_config().hf_config
assert is_deepseek_nsa(hf_config), (
"--enable-hisparse is only supported for DSA (DeepSeek Sparse Attention) models now"
"(e.g., DeepSeek V3.2, GLM-5). "
)
assert (
self.disable_radix_cache
), "Hierarchical sparse attention currently requires --disable-radix-cache."
for attr, label in [
("nsa_prefill_backend", "prefill"),
("nsa_decode_backend", "decode"),
]:
backend = getattr(self, attr)
if backend is not None and backend != "flashmla_sparse":
raise ValueError(
f"HiSparse requires flashmla_sparse NSA {label} backend, "
f"but got --nsa-{label}-backend={backend}. "
f"Please use --nsa-{label}-backend=flashmla_sparse or omit it."
)
if self.kv_cache_dtype != "bfloat16":
raise ValueError(
f"HiSparse requires bfloat16 KV cache, but got --kv-cache-dtype={self.kv_cache_dtype}. "
f"Please use --kv-cache-dtype=bfloat16."
)
assert (
self.schedule_conservativeness >= 0
), "schedule_conservativeness must be non-negative"
if self.model_impl == "mindspore":
assert is_npu(), "MindSpore model impl is only supported on Ascend npu."
# Check metrics labels
if (
not self.tokenizer_metrics_custom_labels_header
and self.tokenizer_metrics_allowed_custom_labels
):
raise ValueError(
"Please set --tokenizer-metrics-custom-labels-header when setting --tokenizer-metrics-allowed-custom-labels."
)
# Check metrics exporters
if self.export_metrics_to_file and self.export_metrics_to_file_dir is None:
raise ValueError(
"--export-metrics-to-file-dir is required when --export-metrics-to-file is enabled"
)
# Check two batch overlap
if self.enable_two_batch_overlap and self.moe_a2a_backend == "none":
raise ValueError(
"When enabling two batch overlap, moe_a2a_backend cannot be 'none'."
)
if (
self.enable_grpc
and self.grpc_port is not None
and self.grpc_port == self.port
):
raise ValueError(
f"SGLANG_GRPC_PORT ({self.grpc_port}) must differ from --port ({self.port})"
)
# TODO: Also validate grpc_port != metrics_http_port and grpc_port != nccl_port
# to avoid opaque bind errors at runtime. Deferred because metrics_http_port
# and nccl_port have dynamic defaults that may not be resolved yet here.
if self.gc_threshold:
if not (1 <= len(self.gc_threshold) <= 3):
raise ValueError(
"When setting gc_threshold, it must contain 1 to 3 integers."
)
def check_lora_server_args(self):
assert self.max_loras_per_batch > 0, "max_loras_per_batch must be positive"
# Enable LoRA if any LoRA paths are provided for backward compatibility.
if self.lora_paths:
if self.enable_lora is None:
self.enable_lora = True
logger.warning(
"--enable-lora is set to True because --lora-paths is provided."
)
elif self.enable_lora is False:
logger.warning(
"--enable-lora is set to False, any provided lora_paths will be ignored."
)
if self.enable_lora:
if self.enable_lora_overlap_loading is None:
self.enable_lora_overlap_loading = False
if self.enable_lora_overlap_loading:
# TODO (glenliu21): use some sort of buffer with eviction instead of enforcing a limit
max_loaded_loras_limit = self.max_loras_per_batch * 2
assert (
self.max_loaded_loras is not None
and self.max_loaded_loras <= max_loaded_loras_limit
), (
"Enabling LoRA overlap loading requires pinning LoRA adapter weights in CPU memory, "
f"so --max-loaded-loras must be less than or equal to double --max-loras-per-batch: {max_loaded_loras_limit}"
)
# Validate compatibility with speculative decoding
if self.speculative_algorithm not in ["NGRAM", None]:
raise ValueError(
"Currently LoRA is only compatible with NGRAM speculative decoding."
)
# Parse lora_paths
if isinstance(self.lora_paths, list):
lora_paths = self.lora_paths
self.lora_paths = []
for lora_path in lora_paths:
if isinstance(lora_path, str):
if "=" in lora_path:
name, path = lora_path.split("=", 1)
lora_ref = LoRARef(
lora_name=name, lora_path=path, pinned=False
)
else:
lora_ref = LoRARef(
lora_name=lora_path, lora_path=lora_path, pinned=False
)
elif isinstance(lora_path, dict):
assert (
"lora_name" in lora_path and "lora_path" in lora_path
), f"When providing LoRA paths as a list of dict, each dict should contain 'lora_name' and 'lora_path' keys. Got: {lora_path}"
lora_ref = LoRARef(
lora_name=lora_path["lora_name"],
lora_path=lora_path["lora_path"],
pinned=lora_path.get("pinned", False),
)
else:
raise ValueError(
f"Invalid type for item in --lora-paths list: {type(lora_path)}. "
"Expected a string or a dictionary."
)
self.lora_paths.append(lora_ref)
elif isinstance(self.lora_paths, dict):
self.lora_paths = [
LoRARef(lora_name=k, lora_path=v, pinned=False)
for k, v in self.lora_paths.items()
]
elif self.lora_paths is None:
self.lora_paths = []
else:
raise ValueError(
f"Invalid type for --lora-paths: {type(self.lora_paths)}. "
"Expected a list or a dictionary."
)
# Normalize target modules to a set; keep {"all"} as a sentinel
# that gets resolved model-awarely in lora_manager.init_lora_shapes().
if self.lora_target_modules:
self.lora_target_modules = set(self.lora_target_modules)
if "all" in self.lora_target_modules:
assert (
len(self.lora_target_modules) == 1
), "If 'all' is specified in --lora-target-modules, it should be the only module specified."
# Ensure sufficient information is provided for LoRA initialization.
assert self.lora_paths or (
self.max_lora_rank and self.lora_target_modules
), "When no initial --lora-paths is provided, you need to specify both --max-lora-rank and --lora-target-modules for LoRA initialization."
# Validate max_loaded_loras
if self.max_loaded_loras is not None:
assert self.max_loaded_loras >= self.max_loras_per_batch, (
"max_loaded_loras should be greater than or equal to max_loras_per_batch. "
f"max_loaded_loras={self.max_loaded_loras}, max_loras_per_batch={self.max_loras_per_batch}"
)
assert len(self.lora_paths) <= self.max_loaded_loras, (
"The number of LoRA paths should not exceed max_loaded_loras. "
f"max_loaded_loras={self.max_loaded_loras}, lora_paths={len(self.lora_paths)}"
)
if self.max_lora_chunk_size is not None:
assert (
16 <= self.max_lora_chunk_size <= 128
and (self.max_lora_chunk_size & (self.max_lora_chunk_size - 1)) == 0
), "--max-lora-chunk-size must be a power of 2 between 16 and 128."
if self.lora_use_virtual_experts:
assert self.lora_backend == "triton", (
"--lora-use-virtual-experts requires --lora-backend triton. "
f"Got: {self.lora_backend}"
)
logger.info("Virtual expert computation enabled.")
def validate_buckets_rule(self, arg_name: str, buckets_rule: List[str]):
if not buckets_rule:
return
assert len(buckets_rule) > 0, f"{arg_name} cannot be empty list"
rule = buckets_rule[0]
assert rule in [
"tse",
"default",
"custom",
], f"Unsupported {arg_name} rule type: '{rule}'. Must be one of: 'tse', 'default', 'custom'"
if rule == "tse":
assert (
len(buckets_rule) == 4
), f"{arg_name} TSE rule requires exactly 4 parameters: ['tse', middle, base, count], got {len(buckets_rule)}"
try:
middle = float(buckets_rule[1])
base = float(buckets_rule[2])
count = int(buckets_rule[3])
except (ValueError, IndexError):
assert (
False
), f"{arg_name} TSE rule parameters must be: ['tse', <float:middle>, <float:base>, <int:count>]"
assert base > 1, f"{arg_name} TSE base must be larger than 1, got: {base}"
assert count > 0, f"{arg_name} TSE count must be positive, got: {count}"
assert middle > 0, f"{arg_name} TSE middle must be positive, got: {middle}"
elif rule == "default":
assert (
len(buckets_rule) == 1
), f"{arg_name} default rule should only have one parameter: ['default'], got {len(buckets_rule)}"
elif rule == "custom":
assert (
len(buckets_rule) >= 2
), f"{arg_name} custom rule requires at least one bucket value: ['custom', value1, ...]"
try:
bucket_values = [float(x) for x in buckets_rule[1:]]
except ValueError:
assert False, f"{arg_name} custom rule bucket values must be numeric"
assert len(set(bucket_values)) == len(
bucket_values
), f"{arg_name} custom rule bucket values should not contain duplicates"
assert all(
val >= 0 for val in bucket_values
), f"{arg_name} custom rule bucket values should be non-negative"
def adjust_mem_fraction_for_vlm(self, model_config):
vision_config = getattr(model_config.hf_config, "vision_config", None)
if vision_config is None:
return
# roughly reduce the mem_fraction_static base on params of Vit
original_server_arg_mem_fraction = self.mem_fraction_static
# a base mem_fraction_static factor for regular Vit
base_mem_fraction_reduction_ratio = 0.95
vit_num_layers = getattr(vision_config, "num_hidden_layers", 24)
vit_hidden_size = getattr(vision_config, "hidden_size", 1024)
# baseline ViT params (ViT-L/14)
baseline_vit_layers = 24
baseline_vit_hidden_size = 1024
# weight params count
current_complexity_score = vit_num_layers * (vit_hidden_size**2)
baseline_complexity_score = baseline_vit_layers * (baseline_vit_hidden_size**2)
complexity_ratio = (
current_complexity_score / baseline_complexity_score
if baseline_complexity_score > 0
else 1.0
)
# every time the complexity grows 100%, adjust final factor for 10%
sensitivity_scale = 0.1
dynamic_adjustment_factor = 1.0 - sensitivity_scale * (complexity_ratio - 1.0)
dynamic_adjustment_factor = max(0.8, min(1.05, dynamic_adjustment_factor))
final_overall_factor = (
base_mem_fraction_reduction_ratio * dynamic_adjustment_factor
)
self.mem_fraction_static = (
original_server_arg_mem_fraction * final_overall_factor
)
def validate_transfer_engine(self):
try:
mooncake_available = importlib.util.find_spec("mooncake.engine") is not None
except (ModuleNotFoundError, ValueError):
mooncake_available = False
if not mooncake_available:
logger.warning(
"Failed to import mooncake.engine. Does not support using TransferEngine as remote instance weight loader backend."
)
return False
elif self.enable_memory_saver:
logger.warning(
"Memory saver is enabled, which is not compatible with TransferEngine. Does not support using TransferEngine as remote instance weight loader backend."
)
return False
else:
return True
@property
def _parsed_modelexpress_config(self) -> dict:
cache = getattr(self, "_mx_config_cache", None)
if cache is not None:
return cache
if self.modelexpress_config is None:
result = {}
elif isinstance(self.modelexpress_config, str):
result = json.loads(self.modelexpress_config)
else:
result = self.modelexpress_config
object.__setattr__(self, "_mx_config_cache", result)
return result
@property
def modelexpress_url(self) -> Optional[str]:
return self._parsed_modelexpress_config.get("url")
@property
def modelexpress_model_name(self) -> Optional[str]:
return self._parsed_modelexpress_config.get("model_name")
@property
def modelexpress_source(self) -> bool:
return self._parsed_modelexpress_config.get("source", False)
def remote_instance_weight_loader_use_transfer_engine(self):
# Use TransferEngine as seed backend.
if self.remote_instance_weight_loader_start_seed_via_transfer_engine:
return True
# ModelExpress source mode also needs TransferEngine init.
if self.modelexpress_source:
return True
# Use TransferEngine as client backend.
elif (
self.load_format == "remote_instance"
and self.remote_instance_weight_loader_backend
in ("transfer_engine", "modelexpress")
):
return True
else:
return False
# NOTE: This is a global variable to hold the server args for scheduler.
_global_server_args: Optional[ServerArgs] = None
def set_global_server_args_for_scheduler(server_args: ServerArgs):
global _global_server_args
_global_server_args = server_args
set_global_server_args_for_tokenizer = set_global_server_args_for_scheduler
def get_global_server_args() -> ServerArgs:
if _global_server_args is None:
raise ValueError("Global server args is not set yet!")
return _global_server_args
def prepare_server_args(argv: List[str]) -> ServerArgs:
"""
Prepare the server arguments from the command line arguments.
Args:
args: The command line arguments. Typically, it should be `sys.argv[1:]`
to ensure compatibility with `parse_args` when no arguments are passed.
Returns:
The server arguments.
"""
parser = argparse.ArgumentParser(prog="sglang serve")
ServerArgs.add_cli_args(parser)
# Check for config file and merge arguments if present
if "--config" in argv:
# Import here to avoid circular imports
from sglang.srt.server_args_config_parser import ConfigArgumentMerger
# Extract boolean actions from the parser to handle them correctly
config_merger = ConfigArgumentMerger(parser)
argv = config_merger.merge_config_with_args(argv)
raw_args = parser.parse_args(argv)
# Set up basic logging before ServerArgs.__post_init__ so that
# logger.info / logger.warning calls there are properly formatted.
logging.basicConfig(
level=getattr(logging, raw_args.log_level.upper()),
format="[%(asctime)s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
force=True,
)
return ServerArgs.from_cli_args(raw_args)
ZMQ_TCP_PORT_DELTA = 233
DP_ATTENTION_HANDSHAKE_PORT_DELTA = 13
@dataclasses.dataclass
class PortArgs:
# The ipc filename for tokenizer to receive inputs from detokenizer (zmq)
tokenizer_ipc_name: str
# The ipc filename for scheduler (rank 0) to receive inputs from tokenizer (zmq)
scheduler_input_ipc_name: str
# The ipc filename for detokenizer to receive inputs from scheduler (zmq)
detokenizer_ipc_name: str
# The port for nccl initialization (torch.dist)
nccl_port: int
# The ipc filename for rpc call between Engine and Scheduler
rpc_ipc_name: str
# The ipc filename for Scheduler to send metrics
metrics_ipc_name: str
# The ipc filename for Tokenizer and worker tokenizer
tokenizer_worker_ipc_name: Optional[str]
@staticmethod
def init_new(
server_args: ServerArgs,
dp_rank: Optional[int] = None,
worker_ports: Optional[List[int]] = None,
) -> PortArgs:
if server_args.nccl_port is None:
nccl_port = get_free_port()
else:
nccl_port = server_args.nccl_port
if server_args.tokenizer_worker_num == 1:
tokenizer_worker_ipc_name = None
else:
tokenizer_worker_ipc_name = (
f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}"
)
if not server_args.enable_dp_attention:
# Normal case, use IPC within a single node
return PortArgs(
tokenizer_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
scheduler_input_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
detokenizer_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
nccl_port=nccl_port,
rpc_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
metrics_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
tokenizer_worker_ipc_name=tokenizer_worker_ipc_name,
)
else:
# DP attention. Use TCP + port to handle both single-node and multi-node.
if server_args.nnodes == 1 and server_args.dist_init_addr is None:
na = NetworkAddress("127.0.0.1", server_args.port + ZMQ_TCP_PORT_DELTA)
else:
na = NetworkAddress.parse(server_args.dist_init_addr)
dist_init_host = na.host
dist_init_port = na.port
port_base = dist_init_port + 1
detokenizer_port = port_base + 1
rpc_port = port_base + 2
metrics_port = port_base + 3
if dp_rank is None:
# TokenizerManager to DataParallelController
scheduler_input_port = port_base + 4
else:
assert worker_ports is not None
scheduler_input_port = worker_ports[dp_rank]
try:
if dp_rank is None:
wait_port_available(dist_init_port, "dist_init_port")
wait_port_available(port_base, "port_base")
wait_port_available(detokenizer_port, "detokenizer_port")
wait_port_available(nccl_port, "nccl_port")
wait_port_available(rpc_port, "rpc_port")
wait_port_available(metrics_port, "metrics_port")
# Check scheduler_input_port only for dp.
# Skip check when using worker_ports since the port is already bound by our ZMQ socket
if dp_rank is None or worker_ports is None:
wait_port_available(scheduler_input_port, "scheduler_input_port")
except ValueError:
logger.exception(
f"Port is already in use. {dist_init_port=} {port_base=} {detokenizer_port=} {nccl_port=} {scheduler_input_port=}"
)
raise
return PortArgs(
tokenizer_ipc_name=NetworkAddress(dist_init_host, port_base).to_tcp(),
scheduler_input_ipc_name=NetworkAddress(
dist_init_host, scheduler_input_port
).to_tcp(),
detokenizer_ipc_name=NetworkAddress(
dist_init_host, detokenizer_port
).to_tcp(),
nccl_port=nccl_port,
rpc_ipc_name=NetworkAddress(dist_init_host, rpc_port).to_tcp(),
metrics_ipc_name=NetworkAddress(dist_init_host, metrics_port).to_tcp(),
tokenizer_worker_ipc_name=tokenizer_worker_ipc_name,
)
class LoRAPathAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
lora_paths = []
if values:
assert isinstance(values, list), "Expected a list of LoRA paths."
for lora_path in values:
lora_path = lora_path.strip()
if lora_path.startswith("{") and lora_path.endswith("}"):
obj = json.loads(lora_path)
assert "lora_path" in obj and "lora_name" in obj, (
f"{repr(lora_path)} looks like a JSON str, "
"but it does not contain 'lora_name' and 'lora_path' keys."
)
lora_paths.append(obj)
else:
lora_paths.append(lora_path)
setattr(namespace, self.dest, lora_paths)
def print_deprecated_warning(message: str):
logger.warning(f"\033[1;33m{message}\033[0m")
class DeprecatedAction(argparse.Action):
def __init__(self, option_strings, dest, nargs=0, **kwargs):
super(DeprecatedAction, self).__init__(
option_strings, dest, nargs=nargs, **kwargs
)
def __call__(self, parser, namespace, values, option_string=None):
print_deprecated_warning(
f"The command line argument '{option_string}' is deprecated and will be removed in future versions."
)
class DeprecatedStoreTrueAction(argparse.Action):
"""Deprecated flag that still stores True and prints a warning."""
def __init__(
self,
option_strings,
dest,
new_flag=None,
nargs=0,
const=True,
default=False,
**kwargs,
):
self.new_flag = new_flag
super().__init__(
option_strings, dest, nargs=nargs, const=const, default=default, **kwargs
)
def __call__(self, parser, namespace, values, option_string=None):
replacement = f" Use '{self.new_flag}' instead." if self.new_flag else ""
print_deprecated_warning(
f"'{option_string}' is deprecated and will be removed in a future release.{replacement}"
)
setattr(namespace, self.dest, True)
def auto_choose_speculative_params(self: ServerArgs):
"""
Automatically choose the parameters for speculative decoding.
You can tune them on your own models and prompts with scripts/playground/bench_speculative.py
"""
hf_config = self.get_model_config().hf_config
arch = hf_config.architectures[0]
if self.speculative_algorithm == "STANDALONE":
# The default value for standalone speculative decoding
return (3, 1, 4)
if arch in ["LlamaForCausalLM"]:
# The default value for llama
return (5, 4, 8)
elif arch in [
"DeepseekV32ForCausalLM",
"DeepseekV3ForCausalLM",
"DeepseekV2ForCausalLM",
"GptOssForCausalLM",
"Glm4MoeForCausalLM",
"Glm4MoeLiteForCausalLM",
"GlmMoeDsaForCausalLM",
"BailingMoeForCausalLM",
"BailingMoeV2ForCausalLM",
"BailingMoeV2_5ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
"MiMoV2ForCausalLM",
"MiMoV2FlashForCausalLM",
]:
return (3, 1, 4)
elif arch in ["Grok1ForCausalLM", "Grok1VForCausalLM"]:
return (5, 4, 8)
else:
# The default value for all other models
return (3, 1, 4)
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