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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.connector import ConnectorType
from sglang.srt.environ import ToolStrictLevel, 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,
configure_ipv6,
cpu_has_amx_support,
get_bool_env_var,
get_device,
get_device_memory_capacity,
get_device_name,
get_device_sm,
get_quantization_config,
is_blackwell_supported,
is_cuda,
is_fa3_default_architecture,
is_flashinfer_available,
is_hip,
is_hopper_with_cuda_12_3,
is_no_spec_infer_or_topk_one,
is_npu,
is_port_available,
is_remote_url,
is_sm90_supported,
is_sm100_supported,
is_sm120_supported,
is_triton_kernels_available,
is_valid_ipv6_address,
json_list_type,
nullable_str,
parse_connector_type,
wait_port_available,
xpu_has_xmx_support,
)
from sglang.srt.utils.hf_transformers_utils import check_gguf_file
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",
"layered",
"flash_rl",
"remote",
"remote_instance",
"fastsafetensors",
"private",
]
QUANTIZATION_CHOICES = [
"awq",
"fp8",
"gptq",
"marlin",
"gptq_marlin",
"awq_marlin",
"bitsandbytes",
"gguf",
"modelopt",
"modelopt_fp8",
"modelopt_fp4",
"petit_nvfp4",
"w8a8_int8",
"w8a8_fp8",
"moe_wna16",
"qoq",
"w4afp8",
"mxfp4",
"auto-round",
"compressed-tensors", # for Ktransformers
"modelslim", # for NPU
"quark_int4fp8_moe",
]
SPECULATIVE_DRAFT_MODEL_QUANTIZATION_CHOICES = [*QUANTIZATION_CHOICES, "unquant"]
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",
]
LORA_BACKEND_CHOICES = ["triton", "csgmv", "ascend", "torch_native"]
DISAGG_TRANSFER_BACKEND_CHOICES = ["mooncake", "nixl", "ascend", "fake"]
ENCODER_TRANSFER_BACKEND_CHOICES = ["zmq_to_scheduler", "zmq_to_tokenizer", "mooncake"]
GRAMMAR_BACKEND_CHOICES = ["xgrammar", "outlines", "llguidance", "none"]
DETERMINISTIC_ATTENTION_BACKEND_CHOICES = ["flashinfer", "fa3", "triton"]
RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND = ["fa3", "triton"]
NSA_PREFILL_CP_SPLIT_CHOICES = ["in-seq-split", "round-robin-split"]
DEFAULT_LORA_EVICTION_POLICY = "lru"
NSA_CHOICES = [
"flashmla_sparse",
"flashmla_kv",
"flashmla_auto",
"fa3",
"tilelang",
"aiter",
]
RADIX_EVICTION_POLICY_CHOICES = ["lru", "lfu"]
RL_ON_POLICY_TARGET_CHOICES = ["fsdp"]
MOE_RUNNER_BACKEND_CHOICES = [
"auto",
"deep_gemm",
"triton",
"triton_kernel",
"flashinfer_trtllm",
"flashinfer_cutlass",
"flashinfer_mxfp4",
"flashinfer_cutedsl",
"cutlass",
]
MOE_A2A_BACKEND_CHOICES = ["none", "deepep", "mooncake", "ascend_fuseep"]
FP8_GEMM_RUNNER_BACKEND_CHOICES = [
"auto",
"deep_gemm",
"flashinfer_trtllm",
"cutlass",
"triton",
"aiter",
]
FP4_GEMM_RUNNER_BACKEND_CHOICES = [
"auto",
"cudnn",
"cutlass",
"trtllm",
]
MAMBA_SSM_DTYPE_CHOICES = ["float32", "bfloat16"]
MAMBA_SCHEDULER_STRATEGY_CHOICES = ["auto", "no_buffer", "extra_buffer"]
# 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_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_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_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)
def add_mamba_ssm_dtype_choices(choices):
MAMBA_SSM_DTYPE_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 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_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
# 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
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
stream_output: 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
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
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
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
collect_tokens_histogram: bool = False
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
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"
# 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
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
# 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: str = "flashmla_sparse"
nsa_decode_backend: str = "fa3"
disable_flashinfer_autotune: bool = False
# 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_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 decoding (ngram)
speculative_ngram_min_match_window_size: int = 1
speculative_ngram_max_match_window_size: int = 12
speculative_ngram_min_bfs_breadth: int = 1
speculative_ngram_max_bfs_breadth: int = 10
speculative_ngram_match_type: Literal["BFS", "PROB"] = "BFS"
speculative_ngram_branch_length: int = 18
speculative_ngram_capacity: int = 10 * 1000 * 1000
enable_multi_layer_eagle: bool = False
# Expert parallelism
ep_size: int = 1
moe_a2a_backend: Literal["none", "deepep", "mooncake", "ascend_fuseep"] = "none"
moe_runner_backend: str = "auto"
flashinfer_mxfp4_moe_precision: Literal["default", "bf16"] = "default"
enable_flashinfer_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"] = None
mooncake_ib_device: Optional[str] = None
# Mamba cache
max_mamba_cache_size: Optional[int] = None
mamba_ssm_dtype: str = "float32"
mamba_full_memory_ratio: float = 0.9
mamba_scheduler_strategy: str = "auto"
mamba_track_interval: int = 256
# 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"
disable_hicache_numa_detect: bool = False
hicache_storage_backend: Optional[str] = None
hicache_storage_prefetch_policy: str = "best_effort"
hicache_storage_backend_extra_config: Optional[str] = None
# Hierarchical sparse attention
hierarchical_sparse_attention_extra_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
# Double Sparsity
enable_double_sparsity: bool = False
ds_channel_config_path: Optional[str] = None
ds_heavy_channel_num: int = 32
ds_heavy_token_num: int = 256
ds_heavy_channel_type: str = "qk"
ds_sparse_decode_threshold: int = 4096
# 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
# Delimiter token ID used to combine Query and Items into a single sequence for multi-item scoring.
# Format: Query<delimiter>Item1<delimiter>Item2<delimiter>...
# This enables efficient batch processing of multiple items against a single query.
multi_item_scoring_delimiter: Optional[Union[int]] = None
# Optimization/debug options
disable_radix_cache: bool = False
cuda_graph_max_bs: Optional[int] = None
cuda_graph_bs: Optional[List[int]] = None
disable_cuda_graph: bool = False
disable_cuda_graph_padding: bool = False
enable_profile_cuda_graph: bool = False
enable_cudagraph_gc: 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
disable_overlap_schedule: bool = False
enable_mixed_chunk: bool = False
enable_dp_attention: bool = False
enable_dp_lm_head: bool = False
enable_two_batch_overlap: bool = False
enable_single_batch_overlap: bool = False
tbo_token_distribution_threshold: float = 0.48
enable_torch_compile: bool = False
enable_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
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
# 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 = "in-seq-split"
enable_fused_qk_norm_rope: bool = False
enable_precise_embedding_interpolation: bool = False
# 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_decode_tp: Optional[int] = None
disaggregation_decode_dp: Optional[int] = None
disaggregation_prefill_pp: Optional[int] = 1
disaggregation_ib_device: Optional[str] = None
disaggregation_decode_enable_offload_kvcache: bool = False
# Enable auto FAKE mode for decode node testing, no need to pass bootstrap_host in request
disaggregation_decode_enable_fake_auto: 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)
# For model weight update and weight loading
custom_weight_loader: Optional[List[str]] = None
weight_loader_disable_mmap: bool = False
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"] = "nccl"
remote_instance_weight_loader_start_seed_via_transfer_engine: bool = False
# For PD-Multiplexing
enable_pdmux: bool = False
pdmux_config_path: Optional[str] = None
sm_group_num: int = 8
# For Multi-Modal
mm_max_concurrent_calls: int = 32
mm_per_request_timeout: float = 10.0
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
# 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
def __post_init__(self):
"""
Orchestrates the handling of various server arguments, ensuring proper configuration and validation.
"""
# Normalize load balancing defaults early (before dummy-model short-circuit).
self._handle_load_balance_method()
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()
# 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()
# 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_kv4_compatibility()
self._handle_page_size()
self._handle_amd_specifics()
self._handle_grammar_backend()
# Handle Hicache settings.
self._handle_hicache()
# Handle data parallelism.
self._handle_data_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 _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
# Backward compat: in PD prefill, legacy "round_robin" means `bootstrap_room` routing.
if (
self.disaggregation_mode == "prefill"
and self.load_balance_method == "round_robin"
):
logger.warning(
"In PD-disaggregation prefill mode, the 'round_robin' load balancing method "
"means `bootstrap_room` routing (use 'follow_bootstrap_room' instead). "
"Falling back to 'follow_bootstrap_room' for backward compatibility."
)
self.load_balance_method = "follow_bootstrap_room"
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]
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()
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 get_bool_env_var("SGLANG_USE_MODELSCOPE"):
if not os.path.exists(self.model_path):
from modelscope import snapshot_download
self.model_path = snapshot_download(self.model_path)
self.tokenizer_path = snapshot_download(
self.tokenizer_path, ignore_patterns=["*.bin", "*.safetensors"]
)
# 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_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_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.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)
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 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 self.enable_piecewise_cuda_graph:
# 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
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 += 2 * 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:
self.adjust_mem_fraction_for_vlm(model_config)
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]
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(640, 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 _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]
if model_arch in [
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
]:
self.dtype = "bfloat16"
if model_arch in [
"DeepseekV3ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
]:
# Set attention backend for DeepSeek
if is_deepseek_nsa(hf_config): # DeepSeek 3.2
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(
f"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
self.kv_cache_dtype = "bf16"
logger.warning(
f"For in-seq split mode, we have the following restrictions: moe_dense_tp_size == 1, moe_a2a_backend == deepep, ep_size == tp_size, kv_cache_dtype == bf16, 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)"
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.")
# For Hopper, we support both bf16 and fp8 kv cache; for Blackwell, we support fp8 only currently
import torch
major, _ = torch.cuda.get_device_capability()
if self.kv_cache_dtype == "auto":
self.kv_cache_dtype = "fp8_e4m3" if major >= 10 else "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"
if self.kv_cache_dtype == "fp8_e4m3":
# flashmla_auto dispatches to flashmla_sparse/flashmla_kv based on hardware and heuristics
self.nsa_prefill_backend = "flashmla_auto"
self.nsa_decode_backend = "flashmla_kv"
logger.warning(
"Setting DSA backend to flashmla_auto for prefill and flashmla_kv for decode for FP8 KV Cache."
)
else:
# set prefill/decode backends to flashmla_sparse for Blackwell.
# The default settings (P=flashmla_sparse, D=fa3) are for Hopper.
if major >= 10:
self.nsa_prefill_backend = "flashmla_sparse"
self.nsa_decode_backend = "flashmla_sparse"
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 self.enable_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)
if self.quantization is None:
# Default DeepSeek V3/R1 native FP8 when not explicitly set,
# Because we need this condition for an assertion in
# flashinfer_trtllm MoE runner backend.
if quant_method is None and model_arch in ["DeepseekV3ForCausalLM"]:
self.quantization = "fp8"
logger.info(
"Quantization not specified, default to fp8 for DeepSeek 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"]
):
self.moe_runner_backend = "flashinfer_trtllm"
logger.info(
"Use flashinfer_trtllm as MoE runner backend on sm100 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."
)
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"
else:
self.attention_backend = "triton"
supported_backends = ["triton", "trtllm_mha", "fa3", "fa4"]
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"
)
if (
prefill_attn_backend == "trtllm_mha"
or decode_attn_backend == "trtllm_mha"
):
# TODO: support swa kv indices translation for trtllm_mha attention backend
self.swa_full_tokens_ratio = 1.0
logger.warning(
"Set swa_full_tokens_ratio to 1.0 for GPT-OSS model with trtllm_mha attention backend."
)
quant_method = get_quantization_config(hf_config)
is_mxfp4_quant_format = quant_method == "mxfp4"
if is_mxfp4_quant_format:
# use bf16 for mxfp4 triton kernels
self.dtype = "bfloat16"
if self.moe_runner_backend == "auto":
if self.enable_piecewise_cuda_graph:
self.moe_runner_backend = "auto"
logger.warning(
"Enable piecewise CUDA graph, enabling auto MOE kernel."
)
elif is_blackwell_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 self.ep_size == 1 and is_triton_kernels_available():
self.moe_runner_backend = "triton_kernel"
logger.warning(
"Detected GPT-OSS model, enabling triton_kernels MOE kernel."
)
if self.moe_runner_backend == "triton_kernel":
assert (
self.ep_size == 1
), "Triton kernel MoE is only supported when ep_size == 1"
elif "MiMoV2FlashForCausalLM" in model_arch:
if self.speculative_algorithm == "EAGLE":
self.enable_multi_layer_eagle = True
logger.info(
"Enable multi-layer EAGLE speculative decoding for MiMoV2FlashForCausalLM 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 MiMoV2FlashForCausalLM model with hierarchical cache"
)
self.disable_hybrid_swa_memory = True
logger.warning(
"Disable hybrid SWA memory for MiMoV2FlashForCausalLM 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",
"trtllm_mha",
"intel_xpu",
}, f"fa3, aiter, triton, 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 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"]:
logger.warning(
f"Disabling Radix Cache for {model_arch} as it is not yet supported."
)
self.disable_radix_cache = True
elif model_arch in ["NemotronHForCausalLM"]:
assert (
not self.enable_mamba_extra_buffer()
), f"mamba extra_buffer is not supported for {model_arch} model"
model_config = self.get_model_config()
if model_config.quantization in [
"modelopt",
"modelopt_fp8",
"modelopt_fp4",
]:
assert model_config.hf_config.mlp_hidden_act == "relu2"
if model_config.quantization == "modelopt":
self.quantization = (
"modelopt_fp4"
if model_config.hf_config.quantization_config["quant_algo"]
== "NVFP4"
else "modelopt_fp8"
)
else:
self.quantization = model_config.quantization
self.moe_runner_backend = "flashinfer_cutlass"
if not self.disable_radix_cache and self.speculative_algorithm is not None:
logger.warning(
"Disabling radix cache since speculative decoding for NemotronHForCausalLM is not supported with radix cache yet."
)
self.disable_radix_cache = True
elif not self.disable_radix_cache:
logger.warning(
"Disabling overlap schedule since MambaRadixCache is not compatible with "
"overlap schedule currently, try to use --disable-radix-cache if overlap schedule is necessary"
)
self.disable_overlap_schedule = True
if is_sm100_supported():
if self.attention_backend is None:
self.attention_backend = "flashinfer"
logger.info(
"Use flashinfer as attention backend on sm100 for NemotronHForCausalLM"
)
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
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",
]:
if is_sm100_supported():
quant_method = get_quantization_config(hf_config)
if self.quantization is None 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}"
)
elif model_arch in ["Qwen3NextForCausalLM"]:
if is_sm100_supported():
quant_method = get_quantization_config(hf_config)
if self.quantization is None 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 Qwen3NextForCausalLM"
)
if self.attention_backend is None:
self.attention_backend = "triton"
logger.info(
"Use triton as attention backend on sm100 for Qwen3NextForCausalLM"
)
if (
not self.disable_radix_cache
and 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
# Mamba radix cache v2
if self.enable_mamba_extra_buffer():
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:
logger.warning(
"Disabling overlap schedule since MambaRadixCache no_buffer is not compatible with "
"overlap schedule currently, try to use --mamba-scheduler-strategy extra_buffer to enable overlap schedule"
)
self.disable_overlap_schedule = True
elif model_arch in [
"FalconH1ForCausalLM",
"JetNemotronForCausalLM",
"JetVLMForConditionalGeneration",
]:
assert (
not self.enable_mamba_extra_buffer()
), f"mamba extra_buffer is not supported for {model_arch} model"
if not self.disable_radix_cache:
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 is_sm100_supported():
if self.attention_backend is None:
self.attention_backend = "triton"
logger.info(
f"Use triton as attention backend on sm100 for {model_arch}"
)
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
if envs.SGLANG_EMBEDDINGS_SPARSE_HEAD.is_set():
self.disable_overlap_schedule = True
logger.warning(
f"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, Qwen3Moe)
# 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",
"GptOssForCausalLM",
"Glm4MoeForCausalLM",
"Qwen3MoeForCausalLM",
]
and (is_sm90_supported() or is_sm100_supported())
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
def _handle_sampling_backend(self):
if self.sampling_backend is None:
self.sampling_backend = (
"flashinfer" if is_flashinfer_available() else "pytorch"
)
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:
"""
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.
"""
if not use_mla_backend:
# MHA architecture
if (
is_hopper_with_cuda_12_3()
and is_no_spec_infer_or_topk_one(self)
and is_fa3_default_architecture(self.model_config.hf_config)
):
self.attention_backend = "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
)
):
self.attention_backend = "trtllm_mha"
elif is_hip():
self.attention_backend = "aiter"
else:
self.attention_backend = (
"flashinfer" if is_flashinfer_available() else "triton"
)
else:
# MLA architecture
if is_hopper_with_cuda_12_3():
self.attention_backend = "fa3"
elif is_sm100_supported():
self.attention_backend = "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:
self.attention_backend = "aiter"
else:
self.attention_backend = "triton"
else:
self.attention_backend = "triton"
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"
# 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). 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"
):
if not is_sm100_supported():
raise ValueError(
"TRTLLM MHA backend is only supported on Blackwell GPUs (SM100). Please use a different 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.attention_backend == "fa4" or self.decode_attention_backend == "fa4":
raise ValueError(
"FA4 backend is only supported for prefill. Please use `--prefill-attention-backend fa4` instead."
)
if self.prefill_attention_backend == "fa4" and not self.use_mla_backend():
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:
self.page_size = 1
def _handle_amd_specifics(self):
if is_hip():
self.triton_attention_num_kv_splits = 16
def _handle_grammar_backend(self):
if self.grammar_backend is None:
self.grammar_backend = "xgrammar"
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.moe_runner_backend == "flashinfer_cutlass":
assert self.quantization in [
"modelopt_fp4",
"modelopt_fp8",
None,
], f"Invalid quantization '{self.quantization}'. \nFlashInfer Cutlass MOE supports only: 'modelopt_fp4', 'modelopt_fp8', 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_trtllm":
assert self.quantization in [
"modelopt_fp4",
"fp8",
"modelopt_fp8",
"compressed-tensors",
None,
], f"Invalid quantization '{self.quantization}'. \nFlashInfer TRTLLM MOE supports only: 'modelopt_fp4', 'fp8', 'modelopt_fp8', '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 get_bool_env_var("SGLANG_CUTLASS_MOE"):
logger.warning(
"SGLANG_CUTLASS_MOE is deprecated, use --moe-runner-backend=cutlass and/or --speculative-moe-runner-backend=cutlass instead"
)
assert (
self.quantization == "fp8"
), "cutlass MoE is only supported with fp8 quantization"
self.moe_runner_backend = "cutlass"
if self.moe_runner_backend == "cutlass" and self.quantization == "fp8":
assert (
self.ep_size == 1
), "FP8 Cutlass MoE is only supported with ep_size == 1"
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 == "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}]."
)
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 == "elasticity_aware"
), "Elastic EP requires eplb_algorithm to be set to 'auto' or 'elasticity_aware'."
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):
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"
)
if (
self.enable_hierarchical_cache
or self.disaggregation_decode_enable_offload_kvcache
) and self.hicache_io_backend == "kernel":
# fix for the compatibility issue with FlashAttention3 decoding and HiCache kernel backend
# Only override when the *effective* decode backend would be FA3.
# Otherwise, respect the user's chosen attention backend (e.g., aiter on ROCm).
effective_decode_backend = (
self.decode_attention_backend
if self.decode_attention_backend is not None
else self.attention_backend
)
if effective_decode_backend == "fa3":
if self.decode_attention_backend is None:
# If decode backend wasn't explicitly set, pick a safe default that works with HiCache kernel IO.
if not self.use_mla_backend():
self.decode_attention_backend = (
"flashinfer" if is_flashinfer_available() else "triton"
)
else:
self.decode_attention_backend = (
"flashinfer" if is_sm100_supported() else "triton"
)
else:
# If user explicitly requested FA3 decode, fall back to direct IO.
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."
)
if self.hicache_storage_backend == "mooncake":
if self.hicache_mem_layout == "layer_first":
if self.hicache_io_backend == "direct":
self.hicache_mem_layout = "page_first_direct"
elif self.hicache_io_backend == "kernel":
self.hicache_mem_layout = "page_first"
logger.warning(
f"Mooncake storage backend does not support layer_first layout, "
f"switching to {self.hicache_mem_layout} layout for {self.hicache_io_backend} io backend"
)
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"
# Avoid using flashinfer_trtllm for speculative MoE runner backend by default
# TODO: Remove this block after verifying no accuracy regression with flashinfer_trtllm speculative backend
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 == "flashinfer_trtllm"
else self.moe_runner_backend
)
else:
assert not MoeRunnerBackend(
self.speculative_moe_runner_backend
).is_flashinfer_trtllm(), "Currently speculative MoE runner backend cannot be flashinfer_trtllm for risk in some draft models."
if self.speculative_algorithm == "NEXTN":
self.speculative_algorithm = "EAGLE"
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",
"BailingMoeForCausalLM",
"BailingMoeV2ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
]:
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 = (
self.speculative_ngram_max_match_window_size
)
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."
)
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 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_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 (
not self.validate_transfer_engine()
and self.remote_instance_weight_loader_backend == "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 _handle_pd_disaggregation(self):
if self.disaggregation_mode == "decode":
assert (
self.disaggregation_decode_tp is None
), "Cannot set --disaggregation-decode-tp for the decode engine."
assert (
self.disaggregation_decode_dp is None
), "Cannot set --disaggregation-decode-dp for the decode engine."
self.disable_radix_cache = True
logger.warning("KV cache is forced as chunk cache for decode server")
elif self.disaggregation_mode == "prefill":
if self.disaggregation_decode_tp is None:
self.disaggregation_decode_tp = self.tp_size
if self.disaggregation_decode_dp is None:
self.disaggregation_decode_dp = self.dp_size
self.disaggregation_prefill_pp = self.pp_size
self.validate_disagg_tp_size(self.tp_size, self.disaggregation_decode_tp)
if not self.enable_piecewise_cuda_graph:
self.disable_cuda_graph = True
logger.warning(
"Cuda graph is disabled for prefill server when piecewise cuda graph is not enabled."
)
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"
)
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):
os.environ["SGLANG_ENABLE_TORCH_COMPILE"] = (
"1" if self.enable_torch_compile else "0"
)
os.environ["SGLANG_MAMBA_SSM_DTYPE"] = self.mamba_ssm_dtype
os.environ["SGLANG_DISABLE_OUTLINES_DISK_CACHE"] = (
"1" if self.disable_outlines_disk_cache else "0"
)
os.environ["SGLANG_ENABLE_DETERMINISTIC_INFERENCE"] = (
"1" if self.enable_deterministic_inference else "0"
)
# Set the highest strict level for Kimi K2 tool calls
if (
self.tool_call_parser == "kimi_k2"
and not envs.SGLANG_TOOL_STRICT_LEVEL.is_set()
):
envs.SGLANG_TOOL_STRICT_LEVEL.set(ToolStrictLevel.PARAMETER)
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.disaggregation_mode == "decode"
and envs.SGLANG_ENABLE_SPEC_V2.get()
):
raise ValueError(
"Spec v2 and decode offload kv cache are incompatible and cannot be enabled together."
)
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
os.environ["SGLANG_VLM_CACHE_SIZE_MB"] = "0"
# TODO remove this environment variable as a whole
os.environ["SGLANG_ENABLE_DETERMINISTIC_INFERENCE"] = "1"
if self.enable_deterministic_inference:
# 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",
]
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 not self.disable_cuda_graph:
if self.cuda_graph_bs != [1]:
logger.warning(
"Cuda graph bs is set to [1] because of using diffusion LLM inference"
)
self.cuda_graph_bs = [1]
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:
logger.warning(
"Radix cache is disabled because of using diffusion LLM inference"
)
self.disable_radix_cache = True
if not self.pp_size > 1:
logger.warning(
"Pipeline parallelism is disabled because of using diffusion LLM inference"
)
self.pp_size = 1
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
# 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-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=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).",
)
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.",
)
# 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=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.",
)
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=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.",
)
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(
"--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.",
)
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'). 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(
"--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(
"--stream-output",
action="store_true",
help="Whether to output as a sequence of disjoint segments.",
)
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(
"--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(
"--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(
"--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="store_true",
default=ServerArgs.collect_tokens_histogram,
help="Collect prompt/generation tokens histogram.",
)
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 interval of decode batch.",
)
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())}.",
)
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(
"--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.",
)
# 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", "triton_attn", "ascend_attn", "aiter_attn"],
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,
)
parser.add_argument(
"--nsa-decode-backend",
default=ServerArgs.nsa_decode_backend,
type=str,
choices=NSA_CHOICES,
)
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), "
"'cutlass' (optimal for Hopper/Blackwell GPUs and high-throughput), "
"'triton' (fallback, widely compatible), "
"'aiter' (ROCm only). "
"NOTE: This replaces the deprecated environment variables "
"SGLANG_ENABLE_FLASHINFER_FP8_GEMM and SGLANG_SUPPORT_CUTLASS_BLOCK_FP8.",
)
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 between cudnn/cutlass based on CUDA/cuDNN version), "
"'cudnn' (cuDNN backend, optimal on CUDA 13+ with cuDNN 9.15+), "
"'cutlass' (CUTLASS backend, optimal on CUDA 12), "
"'trtllm' (TensorRT-LLM backend, requires different weight preparation with shuffling). "
"NOTE: This replaces the deprecated environment variable "
"SGLANG_FLASHINFER_FP4_GEMM_BACKEND.",
)
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=["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-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-match-window-size",
type=int,
default=ServerArgs.speculative_ngram_min_match_window_size,
help="The minimum window size for pattern matching in ngram speculative decoding.",
)
parser.add_argument(
"--speculative-ngram-max-match-window-size",
type=int,
default=ServerArgs.speculative_ngram_max_match_window_size,
help="The maximum window size for pattern matching in ngram speculative decoding.",
)
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-branch-length",
type=int,
default=ServerArgs.speculative_ngram_branch_length,
help="The branch length 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.",
)
# 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(
"--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(
"--deepep-mode",
type=str,
choices=["normal", "low_latency", "auto"],
default="auto",
help="Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch.",
)
parser.add_argument(
"--ep-num-redundant-experts",
type=int,
default=ServerArgs.ep_num_redundant_experts,
help="Allocate this number of redundant experts in expert parallel.",
)
parser.add_argument(
"--ep-dispatch-algorithm",
type=str,
default=ServerArgs.ep_dispatch_algorithm,
help="The algorithm to choose ranks for redundant experts in expert parallel.",
)
parser.add_argument(
"--init-expert-location",
type=str,
default=ServerArgs.init_expert_location,
help="Initial location of EP experts.",
)
parser.add_argument(
"--enable-eplb",
action="store_true",
help="Enable EPLB algorithm",
)
parser.add_argument(
"--eplb-algorithm",
type=str,
default=ServerArgs.eplb_algorithm,
help="Chosen EPLB algorithm",
)
parser.add_argument(
"--eplb-rebalance-num-iterations",
type=int,
default=ServerArgs.eplb_rebalance_num_iterations,
help="Number of iterations to automatically trigger a EPLB re-balance.",
)
parser.add_argument(
"--eplb-rebalance-layers-per-chunk",
type=int,
default=ServerArgs.eplb_rebalance_layers_per_chunk,
help="Number of layers to rebalance per forward pass.",
)
parser.add_argument(
"--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"],
help="Specify the collective communication backend for elastic EP. Currently supports 'mooncake'.",
)
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.",
)
# 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=ServerArgs.mamba_ssm_dtype,
choices=MAMBA_SSM_DTYPE_CHOICES,
help="The data type of the SSM states in mamba cache.",
)
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.",
)
# 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(
"--disable-hicache-numa-detect",
action="store_true",
help="Disable binding the process to the NUMA node closest to the active CUDA device when hierarchical cache is enabled.",
)
parser.add_argument(
"--hicache-storage-backend",
type=str,
choices=["file", "mooncake", "hf3fs", "nixl", "aibrix", "dynamic", "eic"],
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 containing extra configuration for the storage backend.",
)
# Hierarchical sparse attention
parser.add_argument(
"--hierarchical-sparse-attention-extra-config",
type=str,
default=ServerArgs.hierarchical_sparse_attention_extra_config,
help="A dictionary in JSON string format for hierarchical sparse attention configuration. "
"Required fields: algorithm (str), backend (str). "
"All other fields are algorithm-specific and passed to the algorithm constructor. "
'Example: \'{"algorithm": "quest", "backend": "flashattention", "sparsity_ratio": 0.7, "min_sparse_prompt_len": 2048}\'',
)
# 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.",
)
# Double Sparsity
parser.add_argument(
"--enable-double-sparsity",
action="store_true",
help="Enable double sparsity attention",
)
parser.add_argument(
"--ds-channel-config-path",
type=str,
default=ServerArgs.ds_channel_config_path,
help="The path of the double sparsity channel config",
)
parser.add_argument(
"--ds-heavy-channel-num",
type=int,
default=ServerArgs.ds_heavy_channel_num,
help="The number of heavy channels in double sparsity attention",
)
parser.add_argument(
"--ds-heavy-token-num",
type=int,
default=ServerArgs.ds_heavy_token_num,
help="The number of heavy tokens in double sparsity attention",
)
parser.add_argument(
"--ds-heavy-channel-type",
type=str,
default=ServerArgs.ds_heavy_channel_type,
help="The type of heavy channels in double sparsity attention",
)
parser.add_argument(
"--ds-sparse-decode-threshold",
type=int,
default=ServerArgs.ds_sparse_decode_threshold,
help="The minimum decode sequence length required before the double-sparsity backend switches from the dense fallback to the sparse decode kernel.",
)
# 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(
"--multi-item-scoring-delimiter",
type=int,
default=ServerArgs.multi_item_scoring_delimiter,
help="Delimiter token ID for multi-item scoring. Used to combine Query and Items into a single sequence: Query<delimiter>Item1<delimiter>Item2<delimiter>... This enables efficient batch processing of multiple items against a single query.",
)
# 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-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(
"--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(
"--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-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(
"--enable-piecewise-cuda-graph",
action="store_true",
help="Optimize the model with piecewise cuda graph for extend/prefill only. Experimental feature.",
)
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="Enable the NaN detection for debugging purposes.",
)
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(
"--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.",
)
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: 'in-seq-split' (default), 'round-robin-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-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.",
)
# 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.",
)
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-decode-tp",
type=int,
default=ServerArgs.disaggregation_decode_tp,
help="Decode tp size. If not set, it matches the tp size of the current engine. This is only set on the prefill server.",
)
parser.add_argument(
"--disaggregation-decode-dp",
type=int,
default=ServerArgs.disaggregation_decode_dp,
help="Decode dp size. If not set, it matches the dp size of the current engine. This is only set on the prefill server.",
)
parser.add_argument(
"--disaggregation-prefill-pp",
type=int,
default=ServerArgs.disaggregation_prefill_pp,
help="Prefill pp size. If not set, it is default to 1. This is only set on the decode server.",
)
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(
"--disaggregation-decode-enable-fake-auto",
action="store_true",
help="Auto enable FAKE mode for decode node testing, "
"no need to pass bootstrap_host and bootstrap_room in request.",
)
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.",
)
# 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(
"--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"],
default=ServerArgs.remote_instance_weight_loader_backend,
help="The backend for loading weights from remote instance. Can be 'transfer_engine' or 'nccl'. 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.",
)
# 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(
"--mm-max-concurrent-calls",
type=int,
default=ServerArgs.mm_max_concurrent_calls,
help="The max concurrent calls for async mm data processing.",
)
parser.add_argument(
"--mm-per-request-timeout",
type=int,
default=ServerArgs.mm_per_request_timeout,
help="The timeout for each multi-modal request in seconds.",
)
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.",
)
# 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.",
)
@classmethod
def from_cli_args(cls, args: argparse.Namespace):
args.tp_size = args.tensor_parallel_size
args.pp_size = args.pipeline_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):
if is_valid_ipv6_address(self.host):
return f"http://[{self.host}]:{self.port}"
else:
return f"http://{self.host}:{self.port}"
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"
if self.pp_size > 1:
assert (
self.disable_overlap_schedule
and self.speculative_algorithm is None
and not self.enable_mixed_chunk
), "Pipeline parallelism is not compatible with overlap schedule, speculative decoding, mixed chunked prefill."
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
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()
# torch 2.9.1 has compatibility issues with cuDNN 9.14 and below,
# causing extremely slow nn.Conv3d performance.
# TODO(yhyang201): Remove this check when sglang no longer uses torch 2.9.1.
self.check_torch_2_9_1_cudnn_compatibility()
# 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
parts = torch.__version__.split("+", 1)[0].split(".")
major = int(parts[0]) if len(parts) > 0 and parts[0].isdigit() else 0
minor = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else 0
if (major, minor) > (2, 6):
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."
# Check multi-item scoring
if self.multi_item_scoring_delimiter is not None:
assert self.disable_radix_cache, (
"Multi-item scoring requires radix cache to be disabled. "
"Please set --disable-radix-cache when using --multi-item-scoring-delimiter."
)
assert self.chunked_prefill_size == -1, (
"Multi-item scoring requires chunked prefill to be disabled. "
"Please set --chunked-prefill-size -1 when using --multi-item-scoring-delimiter."
)
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'."
)
def check_torch_2_9_1_cudnn_compatibility(self):
if get_bool_env_var("SGLANG_DISABLE_CUDNN_CHECK"):
return
if self.get_model_config().is_multimodal:
import torch
torch_version = torch.__version__.split("+", 1)[0]
if torch_version == "2.9.1":
cudnn_version = None
try:
cudnn_version = torch.backends.cudnn.version()
except Exception:
cudnn_version = None
if cudnn_version is not None:
version_float = float(str(cudnn_version)[:3]) / 100
if version_float < 9.15:
RED = "\033[91m"
BOLD = "\033[1m"
RESET = "\033[0m"
msg = (
f"{RED}{BOLD}"
"CRITICAL WARNING: PyTorch 2.9.1 & CuDNN Compatibility Issue Detected\n"
"--------------------------------------------------------------------------------\n"
f"Current Environment: PyTorch {torch.__version__} | CuDNN {version_float:.2f}\n\n"
"Issue: There is a KNOWN BUG in PyTorch 2.9.1's `nn.Conv3d` implementation\n"
" when used with CuDNN versions older than 9.15. This can cause\n"
" SEVERE PERFORMANCE DEGRADATION and EXCESSIVE MEMORY USAGE.\n\n"
"Reference: https://github.com/pytorch/pytorch/issues/168167\n\n"
"Solution: You MUST upgrade CuDNN to version 9.15+ to ensure correctness.\n\n"
"Run the following command immediately to fix:\n"
" pip install nvidia-cudnn-cu12==9.16.0.29\n\n"
"Or you can disable this check by setting env var SGLANG_DISABLE_CUDNN_CHECK=1\n"
"--------------------------------------------------------------------------------\n"
f"{RESET}"
)
raise RuntimeError(msg)
else:
RED = "\033[91m"
RESET = "\033[0m"
logger.warning(
f"{RED}WARNING: Could not determine CuDNN version for torch==2.9.1. Please ensure CuDNN >= 9.15 to avoid nn.Conv3d bugs.{RESET}"
)
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:
# 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."
)
# Expand target modules
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."
self.lora_target_modules = set(SUPPORTED_LORA_TARGET_MODULES)
# When using the chunked SGMV backend, skip embedding / lm_head layers for now,
# since it does not support these yet (TODO: implement embedding / lm_head support)
if self.lora_backend == "csgmv":
logger.warning(
"LoRA backend 'csgmv' does not yet support embedding or lm_head layers; "
"dropping 'embed_tokens' and 'lm_head' from --lora-target-modules=all. "
"To apply LoRA to these, use --lora-backend triton."
)
self.lora_target_modules.discard("embed_tokens")
self.lora_target_modules.discard("lm_head")
# 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."
def validate_disagg_tp_size(self, prefill_tp: int, decode_tp: int):
larger_tp = max(decode_tp, prefill_tp)
smaller_tp = min(decode_tp, prefill_tp)
assert larger_tp % smaller_tp == 0, (
"Different tp size is supported only when one tp is multiple of the other. "
f"decode_tp={decode_tp}, prefill_tp={prefill_tp}"
)
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):
if importlib.util.find_spec("mooncake.engine") is None:
logger.warning(
f"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
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
# Use TransferEngine as client backend.
elif (
self.load_format == "remote_instance"
and self.remote_instance_weight_loader_backend == "transfer_engine"
):
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()
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)
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 = server_args.port + random.randint(100, 1000)
while True:
if is_port_available(nccl_port):
break
if nccl_port < 60000:
nccl_port += 42
else:
nccl_port -= 43
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:
dist_init_addr = ("127.0.0.1", server_args.port + ZMQ_TCP_PORT_DELTA)
elif server_args.dist_init_addr.startswith("["): # ipv6 address
port_num, host = configure_ipv6(server_args.dist_init_addr)
dist_init_addr = (host, str(port_num))
else:
dist_init_addr = server_args.dist_init_addr.split(":")
assert (
len(dist_init_addr) == 2
), "please provide --dist-init-addr as host:port of head node"
dist_init_host, dist_init_port = dist_init_addr
dist_init_port = int(dist_init_port)
port_base = dist_init_port + 1
detokenizer_port = port_base + 1
rpc_port = port_base + 2
metrics_ipc_name = 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_ipc_name, "metrics_ipc_name")
# 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 as e:
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=f"tcp://{dist_init_host}:{port_base}",
scheduler_input_ipc_name=f"tcp://{dist_init_host}:{scheduler_input_port}",
detokenizer_ipc_name=f"tcp://{dist_init_host}:{detokenizer_port}",
nccl_port=nccl_port,
rpc_ipc_name=f"tcp://{dist_init_host}:{rpc_port}",
metrics_ipc_name=f"tcp://{dist_init_host}:{metrics_ipc_name}",
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."
)
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",
"BailingMoeForCausalLM",
"BailingMoeV2ForCausalLM",
"MistralLarge3ForCausalLM",
"PixtralForConditionalGeneration",
"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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