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Clean up server args (#8161)

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This commit is contained in:
Lianmin Zheng
2025-07-19 11:32:52 -07:00
committed by GitHub
parent 1b427dae02
commit bb0e8a32b5
8 changed files with 389 additions and 343 deletions
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8
python/sglang/srt/configs/model_config.py
View File

@@ -53,7 +53,7 @@ class ModelConfig:
trust_remote_code: bool = True,
revision: Optional[str] = None,
context_length: Optional[int] = None,
model_override_args: Optional[str] = None,
model_override_args: str = "{}",
is_embedding: Optional[bool] = None,
enable_multimodal: Optional[bool] = None,
dtype: str = "auto",
@@ -61,13 +61,13 @@ class ModelConfig:
override_config_file: Optional[str] = None,
is_draft_model: bool = False,
hybrid_kvcache_ratio: Optional[float] = None,
impl: Union[str, ModelImpl] = ModelImpl.AUTO,
model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
) -> None:
self.model_path = model_path
self.revision = revision
self.quantization = quantization
self.impl = impl
self.model_impl = model_impl
# Parse args
self.maybe_pull_model_tokenizer_from_remote()
@@ -286,7 +286,7 @@ class ModelConfig:
dtype=server_args.dtype,
quantization=server_args.quantization,
hybrid_kvcache_ratio=server_args.hybrid_kvcache_ratio,
impl=server_args.impl,
model_impl=server_args.model_impl,
**kwargs,
)

3
python/sglang/srt/managers/scheduler.py
View File

@@ -1389,8 +1389,6 @@ class Scheduler(
f += f"#running-req: {running_bs}, "
f += f"#queue-req: {len(self.waiting_queue)}, "
f += f"timestamp: {datetime.datetime.now().isoformat()}"
logger.info(f)
if self.enable_metrics:
@@ -1471,7 +1469,6 @@ class Scheduler(
f"cuda graph: {can_run_cuda_graph}, "
f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
f"#queue-req: {len(self.waiting_queue)}, "
f"timestamp: {datetime.datetime.now().isoformat()}"
)
logger.info(msg)

8
python/sglang/srt/model_loader/utils.py
View File

@@ -56,14 +56,14 @@ def resolve_transformers_arch(model_config: ModelConfig, architectures: list[str
"if the model is custom)."
)
model_module = auto_modules["AutoModel"]
if model_config.impl == ModelImpl.TRANSFORMERS:
if model_config.model_impl == ModelImpl.TRANSFORMERS:
if not model_module.is_backend_compatible():
raise ValueError(
f"The Transformers implementation of {arch} is not "
"compatible with vLLM."
"compatible with SGLang."
)
architectures[i] = "TransformersForCausalLM"
if model_config.impl == ModelImpl.AUTO:
if model_config.model_impl == ModelImpl.AUTO:
if not model_module.is_backend_compatible():
raise ValueError(
f"{arch} has no SGlang implementation and the Transformers "
@@ -97,7 +97,7 @@ def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module],
supported_archs = ModelRegistry.get_supported_archs()
is_native_supported = any(arch in supported_archs for arch in architectures)
if not is_native_supported or model_config.impl == ModelImpl.TRANSFORMERS:
if not is_native_supported or model_config.model_impl == ModelImpl.TRANSFORMERS:
architectures = resolve_transformers_arch(model_config, architectures)
return ModelRegistry.resolve_model_cls(architectures)

556
python/sglang/srt/server_args.py
View File

@@ -20,6 +20,7 @@ import logging
import os
import random
import tempfile
from token import OP
from typing import List, Literal, Optional, Union
from sglang.srt.hf_transformers_utils import check_gguf_file, get_config
@@ -46,31 +47,28 @@ class ServerArgs:
tokenizer_path: Optional[str] = None
tokenizer_mode: str = "auto"
skip_tokenizer_init: bool = False
skip_server_warmup: bool = False
load_format: str = "auto"
model_loader_extra_config: str = "{}"
trust_remote_code: bool = False
dtype: str = "auto"
kv_cache_dtype: str = "auto"
quantization: Optional[str] = None
quantization_param_path: Optional[str] = None
context_length: Optional[int] = None
device: Optional[str] = None
served_model_name: Optional[str] = None
chat_template: Optional[str] = None
completion_template: Optional[str] = None
is_embedding: bool = False
enable_multimodal: Optional[bool] = None
revision: Optional[str] = None
hybrid_kvcache_ratio: Optional[float] = None
swa_full_tokens_ratio: float = 0.8
impl: str = "auto"
model_impl: str = "auto"
# Port for the HTTP server
# HTTP server
host: str = "127.0.0.1"
port: int = 30000
skip_server_warmup: bool = False
warmups: Optional[str] = None
nccl_port: Optional[int] = None
# Quantization and data type
dtype: str = "auto"
quantization: Optional[str] = None
quantization_param_path: Optional[str] = None
kv_cache_dtype: str = "auto"
# Memory and scheduling
mem_fraction_static: Optional[float] = None
max_running_requests: Optional[int] = None
@@ -81,8 +79,12 @@ class ServerArgs:
schedule_conservativeness: float = 1.0
cpu_offload_gb: int = 0
page_size: int = 1
hybrid_kvcache_ratio: Optional[float] = None
swa_full_tokens_ratio: float = 0.8
disable_hybrid_swa_memory: bool = False
# Other runtime options
# Runtime options
device: Optional[str] = None
tp_size: int = 1
pp_size: int = 1
max_micro_batch_size: Optional[int] = None
@@ -107,8 +109,8 @@ class ServerArgs:
enable_metrics: bool = False
enable_metrics_for_all_schedulers: bool = False
bucket_time_to_first_token: Optional[List[float]] = None
bucket_e2e_request_latency: 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
decode_log_interval: int = 40
enable_request_time_stats_logging: bool = False
@@ -116,6 +118,9 @@ class ServerArgs:
# API related
api_key: Optional[str] = None
served_model_name: Optional[str] = None
chat_template: Optional[str] = None
completion_template: Optional[str] = None
file_storage_path: str = "sglang_storage"
enable_cache_report: bool = False
reasoning_parser: Optional[str] = None
@@ -179,6 +184,14 @@ class ServerArgs:
deepep_config: Optional[str] = None
moe_dense_tp_size: Optional[int] = None
# Hierarchical cache
enable_hierarchical_cache: bool = False
hicache_ratio: float = 2.0
hicache_size: int = 0
hicache_write_policy: str = "write_through_selective"
hicache_io_backend: str = ""
hicache_storage_backend: Optional[str] = None
# Double Sparsity
enable_double_sparsity: bool = False
ds_channel_config_path: Optional[str] = None
@@ -200,7 +213,6 @@ class ServerArgs:
disable_custom_all_reduce: bool = False
enable_mscclpp: bool = False
disable_overlap_schedule: bool = False
disable_overlap_cg_plan: bool = False
enable_mixed_chunk: bool = False
enable_dp_attention: bool = False
enable_dp_lm_head: bool = False
@@ -217,20 +229,12 @@ class ServerArgs:
enable_memory_saver: bool = False
allow_auto_truncate: bool = False
enable_custom_logit_processor: bool = False
enable_hierarchical_cache: bool = False
hicache_ratio: float = 2.0
hicache_size: int = 0
hicache_write_policy: str = "write_through_selective"
hicache_io_backend: str = ""
hicache_storage_backend: Optional[str] = None
flashinfer_mla_disable_ragged: bool = False
disable_shared_experts_fusion: bool = False
disable_chunked_prefix_cache: bool = False
disable_fast_image_processor: bool = False
enable_return_hidden_states: bool = False
enable_triton_kernel_moe: bool = False
warmups: Optional[str] = None
disable_hybrid_swa_memory: bool = False
# Debug tensor dumps
debug_tensor_dump_output_folder: Optional[str] = None
@@ -238,7 +242,7 @@ class ServerArgs:
debug_tensor_dump_inject: bool = False
debug_tensor_dump_prefill_only: bool = False
# For PD disaggregation: can be "null" (not disaggregated), "prefill" (prefill-only), or "decode" (decode-only)
# PD disaggregation: can be "null" (not disaggregated), "prefill" (prefill-only), or "decode" (decode-only)
disaggregation_mode: str = "null"
disaggregation_transfer_backend: str = "mooncake"
disaggregation_bootstrap_port: int = 8998
@@ -273,6 +277,7 @@ class ServerArgs:
logger.warning(
f"Flashinfer MoE is enabled. Shared expert fusion is disabled."
)
# Set missing default values
if self.tokenizer_path is None:
self.tokenizer_path = self.model_path
@@ -333,56 +338,12 @@ class ServerArgs:
self.mem_fraction_static = 0.88
# Lazy init to avoid circular import
# Multimodal models need more memory for the image processor
from sglang.srt.configs.model_config import ModelConfig
# Multimodal models need more memory for the image processor
model_config = ModelConfig.from_server_args(self)
vision_config = getattr(model_config.hf_config, "vision_config", None)
if model_config.is_multimodal and vision_config:
# 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
)
logger.warning(
f"Multimodal model: Dynamically adjusted --mem-fraction-static "
f"from: {original_server_arg_mem_fraction:.3f} to: {self.mem_fraction_static:.3f}."
)
if model_config.is_multimodal:
self.adjust_mem_fraction_for_vlm(model_config)
# Set chunked prefill size, which depends on the gpu memory capacity
if self.chunked_prefill_size is None:
@@ -406,23 +367,6 @@ class ServerArgs:
else:
self.cuda_graph_max_bs = 80
assert self.moe_dense_tp_size in {
1,
None,
}, "moe_dense_tp_size only support 1 and None currently"
if self.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":
logger.warning(
"Cutlass MLA only supports a page_size of 128, change page_size to 128."
)
self.page_size = 128
# Set kernel backends for hpu device
if self.device == "hpu":
self.attention_backend = "torch_native"
@@ -451,6 +395,18 @@ class ServerArgs:
)
self.page_size = 128
if self.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":
logger.warning(
"Cutlass MLA only supports a page_size of 128, change page_size to 128."
)
self.page_size = 128
# Choose grammar backend
if self.grammar_backend is None:
self.grammar_backend = "xgrammar"
@@ -482,12 +438,6 @@ class ServerArgs:
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.pp_size > 1:
self.disable_overlap_schedule = True
logger.warning(
"Pipeline parallelism is incompatible with overlap schedule."
)
if self.enable_eplb and (self.expert_distribution_recorder_mode is None):
self.expert_distribution_recorder_mode = "stat"
logger.info(
@@ -513,6 +463,13 @@ class ServerArgs:
elif self.expert_distribution_recorder_mode is not None:
self.expert_distribution_recorder_buffer_size = 1000
# Pipeline parallelism
if self.pp_size > 1:
self.disable_overlap_schedule = True
logger.warning(
"Pipeline parallelism is incompatible with overlap schedule."
)
# Speculative Decoding
if self.speculative_algorithm == "NEXTN":
# NEXTN shares the same implementation of EAGLE
@@ -533,8 +490,7 @@ class ServerArgs:
"eagle speculative decoding."
)
model_arch = get_model_arch(self)
model_arch = self.get_hf_config().architectures[0]
if model_arch == "DeepseekV3ForCausalLM":
# Auto set draft_model_path DeepSeek-V3/R1
if self.speculative_draft_model_path is None:
@@ -624,17 +580,9 @@ class ServerArgs:
if self.custom_weight_loader is None:
self.custom_weight_loader = []
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}"
)
@staticmethod
def add_cli_args(parser: argparse.ArgumentParser):
# Model and port args
# Model and tokenizer
parser.add_argument(
"--model-path",
"--model",
@@ -648,24 +596,6 @@ class ServerArgs:
default=ServerArgs.tokenizer_path,
help="The path of the tokenizer.",
)
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(
"--nccl-port",
type=int,
default=ServerArgs.nccl_port,
help="The port for NCCL distributed environment setup. Defaults to a random port.",
)
parser.add_argument(
"--tokenizer-mode",
type=str,
@@ -680,11 +610,6 @@ class ServerArgs:
action="store_true",
help="If set, skip init tokenizer and pass input_ids in generate request.",
)
parser.add_argument(
"--skip-server-warmup",
action="store_true",
help="If set, skip warmup.",
)
parser.add_argument(
"--load-format",
type=str,
@@ -730,6 +655,77 @@ class ServerArgs:
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 '
"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(
"--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.",
)
# Quantization and data type
parser.add_argument(
"--dtype",
type=str,
@@ -744,13 +740,6 @@ class ServerArgs:
'* "float" is shorthand for FP32 precision.\n'
'* "float32" for FP32 precision.',
)
parser.add_argument(
"--kv-cache-dtype",
type=str,
default=ServerArgs.kv_cache_dtype,
choices=["auto", "fp8_e5m2", "fp8_e4m3"],
help='Data type for kv cache storage. "auto" will use model data type. "fp8_e5m2" and "fp8_e4m3" is supported for CUDA 11.8+.',
)
parser.add_argument(
"--quantization",
type=str,
@@ -785,65 +774,11 @@ class ServerArgs:
"default to 1.0, which may cause accuracy issues. ",
)
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(
"--device",
"--kv-cache-dtype",
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(
"--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(
"--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(
"--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(
"--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(
"--impl",
type=str,
default=ServerArgs.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 '
"implementation.\n",
default=ServerArgs.kv_cache_dtype,
choices=["auto", "fp8_e5m2", "fp8_e4m3"],
help='Data type for kv cache storage. "auto" will use model data type. "fp8_e5m2" and "fp8_e4m3" is supported for CUDA 11.8+.',
)
# Memory and scheduling
@@ -928,7 +863,13 @@ class ServerArgs:
help="Disable the hybrid SWA memory.",
)
# Other runtime options
# 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",
@@ -970,7 +911,7 @@ class ServerArgs:
"--constrained-json-whitespace-pattern",
type=str,
default=ServerArgs.constrained_json_whitespace_pattern,
help=r"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 ]*",
help="(outlines backend 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(
"--watchdog-timeout",
@@ -1083,12 +1024,6 @@ class ServerArgs:
default=ServerArgs.collect_tokens_histogram,
help="Collect prompt/generation tokens histogram.",
)
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(
"--decode-log-interval",
type=int,
@@ -1101,6 +1036,12 @@ class ServerArgs:
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.",
)
# API related
parser.add_argument(
@@ -1109,6 +1050,24 @@ class ServerArgs:
default=ServerArgs.api_key,
help="Set API key of the server. It is also used in the OpenAI API compatible server.",
)
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(
"--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(
"--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,
@@ -1427,6 +1386,46 @@ class ServerArgs:
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.",
)
# 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"],
default=ServerArgs.hicache_io_backend,
help="The IO backend for KV cache transfer between CPU and GPU",
)
parser.add_argument(
"--hicache-storage-backend",
type=str,
choices=["file"], # todo, mooncake
default=ServerArgs.hicache_storage_backend,
help="The storage backend for hierarchical KV cache.",
)
# Double Sparsity
parser.add_argument(
"--enable-double-sparsity",
@@ -1619,44 +1618,6 @@ class ServerArgs:
action="store_true",
help="Enable users to pass custom logit processors to the server (disabled by default for security)",
)
parser.add_argument(
"--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"],
default=ServerArgs.hicache_io_backend,
help="The IO backend for KV cache transfer between CPU and GPU",
)
parser.add_argument(
"--hicache-storage-backend",
type=str,
choices=["file"], # todo, mooncacke
default=ServerArgs.hicache_storage_backend,
help="The storage backend for hierarchical KV cache.",
)
parser.add_argument(
"--flashinfer-mla-disable-ragged",
action="store_true",
@@ -1687,13 +1648,6 @@ class ServerArgs:
action="store_true",
help="Use triton moe grouped gemm kernel.",
)
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",
)
# Debug tensor dumps
parser.add_argument(
@@ -1720,7 +1674,7 @@ class ServerArgs:
help="Only dump the tensors for prefill requests (i.e. batch size > 1).",
)
# Disaggregation
# PD disaggregation
parser.add_argument(
"--disaggregation-mode",
type=str,
@@ -1779,6 +1733,8 @@ class ServerArgs:
default=None,
help="The URL of the PD disaggregation load balancer. If set, the prefill/decode server will register with the load balancer.",
)
# Custom weight loader
parser.add_argument(
"--custom-weight-loader",
type=str,
@@ -1791,6 +1747,8 @@ class ServerArgs:
action="store_true",
help="Enable PD-Multiplexing, PD running on greenctx stream.",
)
# For PD-Multiplexing
parser.add_argument(
"--sm-group-num",
type=int,
@@ -1818,6 +1776,17 @@ class ServerArgs:
else:
return f"http://{self.host}:{self.port}"
def get_hf_config(self):
kwargs = {}
hf_config = get_config(
self.model_path,
trust_remote_code=self.trust_remote_code,
revision=self.revision,
model_override_args=json.loads(self.json_model_override_args),
**kwargs,
)
return hf_config
def check_server_args(self):
assert (
self.tp_size * self.pp_size
@@ -1842,6 +1811,11 @@ class ServerArgs:
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"
if isinstance(self.lora_paths, list):
lora_paths = self.lora_paths
self.lora_paths = {}
@@ -1852,6 +1826,56 @@ class ServerArgs:
else:
self.lora_paths[lora_path] = lora_path
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 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
)
logger.warning(
f"Multimodal model: Dynamically adjusted --mem-fraction-static "
f"from: {original_server_arg_mem_fraction:.3f} to: {self.mem_fraction_static:.3f}."
)
def prepare_server_args(argv: List[str]) -> ServerArgs:
"""
@@ -1895,16 +1919,16 @@ class PortArgs:
@staticmethod
def init_new(server_args, dp_rank: Optional[int] = None) -> "PortArgs":
if server_args.nccl_port is None:
port = server_args.port + random.randint(100, 1000)
nccl_port = server_args.port + random.randint(100, 1000)
while True:
if is_port_available(port):
if is_port_available(nccl_port):
break
if port < 60000:
port += 42
if nccl_port < 60000:
nccl_port += 42
else:
port -= 43
nccl_port -= 43
else:
port = server_args.nccl_port
nccl_port = server_args.nccl_port
if not server_args.enable_dp_attention:
# Normal case, use IPC within a single node
@@ -1912,7 +1936,7 @@ class 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=port,
nccl_port=nccl_port,
rpc_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
metrics_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
)
@@ -1942,7 +1966,7 @@ class 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}:{port_base + 1}",
nccl_port=port,
nccl_port=nccl_port,
rpc_ipc_name=f"tcp://{dist_init_host}:{port_base + 2}",
metrics_ipc_name=f"tcp://{dist_init_host}:{port_base + 3}",
)
@@ -1969,31 +1993,13 @@ class DeprecatedAction(argparse.Action):
raise ValueError(self.help)
def get_model_arch(args: ServerArgs):
hf_config = get_config(
args.model_path,
trust_remote_code=args.trust_remote_code,
revision=args.revision,
model_override_args=json.loads(args.json_model_override_args),
)
return hf_config.architectures[0]
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
"""
kwargs = {}
hf_config = get_config(
self.model_path,
trust_remote_code=self.trust_remote_code,
revision=self.revision,
model_override_args=json.loads(self.json_model_override_args),
**kwargs,
)
hf_config = self.get_hf_config()
arch = hf_config.architectures[0]
if arch in ["LlamaForCausalLM"]:

4
python/sglang/test/runners.py
View File

@@ -481,7 +481,7 @@ class SRTRunner:
torch_dtype: torch.dtype,
model_type: str,
tp_size: int = 1,
impl: str = "auto",
model_impl: str = "auto",
port: int = DEFAULT_PORT_FOR_SRT_TEST_RUNNER,
lora_paths: List[str] = None,
max_loras_per_batch: int = 4,
@@ -525,7 +525,7 @@ class SRTRunner:
tp_size=tp_size,
dtype=get_dtype_str(torch_dtype),
port=port,
impl=impl,
model_impl=model_impl,
torchao_config=torchao_config,
mem_fraction_static=mem_fraction_static,
trust_remote_code=trust_remote_code,