[Perf] Fork-safe import: no CUDA context at import time, lighter argument parsing (#40201)

python/sglang/cli/utils.py

@@ -4,8 +4,6 @@ import os
 import subprocess
 from functools import lru_cache
 
-from huggingface_hub import HfApi
-
 from sglang.srt.environ import envs
 from sglang.utils import (
     has_diffusion_overlay_registry_match,
@@ -24,7 +22,17 @@ def _is_overlay_diffusion_model(model_path: str) -> bool:
     return has_diffusion_overlay_registry_match(model_path, _load_overlay_registry())
 
 
+def _diffusion_deps_available() -> bool:
+    # Locating diffusers is cheap; importing the registry costs ~2 s and then
+    # fails anyway without it. A false positive is caught by the caller.
+    import importlib.util
+
+    return importlib.util.find_spec("diffusers") is not None
+
+
 def _is_diffusion_model_from_registry(model_path: str) -> bool:
+    if not _diffusion_deps_available():
+        return False
     try:
         from sglang.multimodal_gen.registry import is_registered_diffusion_model_path
     except ImportError:
@@ -49,6 +57,8 @@ def _is_diffusers_model_dir(model_dir: str) -> bool:
 def _is_gated_diffusion_repo(repo_id: str) -> bool:
     """Query HF model card metadata to check if a gated repo is a diffusers model."""
     try:
+        from huggingface_hub import HfApi  # lazy: ~0.3 s at CLI entry otherwise
+
         info = HfApi().model_info(repo_id)
         return getattr(info, "library_name", None) == "diffusers"
     except Exception:

python/sglang/srt/distributed/parallel_state.py

@@ -107,7 +107,9 @@ def get_torch_distributed_pg_options(group_name=None):
 
 @dataclass
 class GraphCaptureContext:
-    stream: torch.get_device_module().Stream
+    # Evaluating torch.get_device_module() at import marks the process unsafe
+    # to fork, and a child then fails in cuInit; torch.Stream is its base.
+    stream: torch.Stream
 
 
 @dataclass

python/sglang/srt/function_call/parser_names.py

@@ -0,0 +1,51 @@
+"""Tool-call parser names, kept in a dependency-free module.
+
+`server_args` needs these for the `--tool-call-parser` CLI choices, and
+importing `function_call_parser` for them costs seconds (OpenAI protocol
+models -> xgrammar -> transformers -> torch.distributed). A unit test checks
+this list against `FunctionCallParser.ToolCallParserEnum`; add a name here
+when you register a parser.
+"""
+
+TOOL_CALL_PARSER_NAMES = [
+    "apertus2509",
+    "cohere_command4",
+    "deepseekv3",
+    "deepseekv31",
+    "deepseekv32",
+    "deepseekv4",
+    "deepseekv41",
+    "dots",
+    "glm",
+    "glm45",
+    "glm47",
+    "gpt-oss",
+    "k2_horizon",
+    "kimi_k2",
+    "kimi_k3",
+    "lfm2",
+    "ling3",
+    "llama3",
+    "mimo",
+    "minicpm5",
+    "mistral",
+    "muse",
+    "poolside_v1",
+    "pythonic",
+    "qwen",
+    "qwen25",
+    "qwen3_coder",
+    "spark25",
+    "step3",
+    "step3p5",
+    "minimax-m2",
+    "minimax-m3",
+    "nanbeige",
+    "trinity",
+    "interns1",
+    "hermes",
+    "hunyuan",
+    "gigachat3",
+    "gemma4",
+    "inkling",
+]

python/sglang/srt/parser/reasoning_parser_names.py

@@ -0,0 +1,39 @@
+"""Reasoning parser names, kept in a dependency-free module (see
+`function_call/parser_names.py`). A unit test checks that this list matches
+`ReasoningParser.DetectorMap`; add a name here when you register a parser.
+"""
+
+REASONING_PARSER_NAMES = [
+    "apertus2509",
+    "deepseek-r1",
+    "deepseek-v3",
+    "deepseek-v4",
+    "deepseek-v41",
+    "dots",
+    "glm45",
+    "ling3",
+    "hunyuan",
+    "gpt-oss",
+    "k2_horizon",
+    "kimi",
+    "kimi_k2",
+    "kimi_k3",
+    "mimo",
+    "muse",
+    "poolside_v1",
+    "qwen3",
+    "qwen3-thinking",
+    "minimax",
+    "minimax-append-think",
+    "minimax-m3",
+    "nanbeige",
+    "step3",
+    "step3p5",
+    "mistral",
+    "nemotron_3",
+    "granite_thinking_parser",
+    "interns1",
+    "gemma4",
+    "inkling",
+    "cohere_command4",
+]

python/sglang/srt/server_args.py

@@ -37,14 +37,15 @@ import argparse
 import copy
 import dataclasses
 import functools
+import importlib
 import logging
+import sys
 import tempfile
 import uuid
 from typing import Any, NoReturn
 
 import msgspec
 
-from sglang.kernels.ops.kv_canary.consts import RealKvHashMode
 from sglang.srt.arg_groups.arg_utils import (
     add_cli_args_from_dataclass,
     is_record,
@@ -60,14 +61,41 @@ from sglang.srt.arg_groups.overrides import (
     resolving_view,
 )
 from sglang.srt.environ import envs
-from sglang.srt.function_call.function_call_parser import FunctionCallParser
-from sglang.srt.parser.reasoning_parser import ReasoningParser
 from sglang.srt.runtime_context import get_platform, publish
 from sglang.srt.speculative.decoupled_spec_io import DecoupledSpecIpcConfig
 from sglang.srt.utils.network import NetworkAddress, get_free_port, wait_port_available
 
 logger = logging.getLogger(__name__)
 
+
+def _reasoning_parser_choices():
+    # Importing the registry here costs seconds in every process that parses
+    # arguments; a plugin that registered a parser has already imported it.
+    module = sys.modules.get("sglang.srt.parser.reasoning_parser")
+    if module is not None:
+        return list(module.ReasoningParser.DetectorMap)
+    from sglang.srt.parser.reasoning_parser_names import REASONING_PARSER_NAMES
+
+    return list(REASONING_PARSER_NAMES)
+
+
+def _tool_call_parser_choices():
+    module = sys.modules.get("sglang.srt.function_call.function_call_parser")
+    if module is not None:
+        return list(module.FunctionCallParser.ToolCallParserEnum)
+    from sglang.srt.function_call.parser_names import TOOL_CALL_PARSER_NAMES
+
+    return list(TOOL_CALL_PARSER_NAMES)
+
+
+def _real_kv_hash_modes():
+    # Lazy: this pulls the whole sglang.kernels package (~2 s) into every
+    # process that imports server_args, most of which never use it.
+    from sglang.kernels.ops.kv_canary.consts import RealKvHashMode
+
+    return list(RealKvHashMode)
+
+
 # Re-exported. These were importable from this module while the field
 # declarations that used them lived here; the declarations moved to
 # `arg_groups/fields/` but out-of-tree code -- and `tokenizer_control_mixin`
@@ -172,6 +200,23 @@ from sglang.srt.utils.common import (  # noqa: F401
     nullable_str,
 )
 
+# Re-exported like the imports above, but resolved on first use: importing them
+# eagerly is what the choices helpers avoid, and most processes never read them.
+_LAZY_REEXPORTS = {
+    "FunctionCallParser": "sglang.srt.function_call.function_call_parser",
+    "ReasoningParser": "sglang.srt.parser.reasoning_parser",
+    "RealKvHashMode": "sglang.kernels.ops.kv_canary.consts",
+}
+
+
+def __getattr__(name: str) -> Any:
+    module_name = _LAZY_REEXPORTS.get(name)
+    if module_name is None:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+    value = getattr(importlib.import_module(module_name), name)
+    globals()[name] = value
+    return value
+
 
 def _plain(value: Any) -> Any:
     """``asdict``'s conversion, applied to one value: a record -- Struct or
@@ -362,7 +407,7 @@ class ServerArgs:
             help="Choose the kernels for sampling layers.",
         )
 
-        reasoning_parser_choices = list(ReasoningParser.DetectorMap.keys())
+        reasoning_parser_choices = _reasoning_parser_choices()
         parser.add_argument(
             "--reasoning-parser",
             type=str,
@@ -372,7 +417,7 @@ class ServerArgs:
             f"Use 'auto' to detect from chat template. "
             f"Options include: {reasoning_parser_choices}.",
         )
-        tool_call_parser_choices = list(FunctionCallParser.ToolCallParserEnum.keys())
+        tool_call_parser_choices = _tool_call_parser_choices()
         parser.add_argument(
             "--tool-call-parser",
             type=str,
@@ -386,7 +431,7 @@ class ServerArgs:
             "--kv-canary-real-data",
             type=str,
             default=_declared_default("kv_canary_real_data"),
-            choices=[m.name.lower() for m in RealKvHashMode],
+            choices=[m.name.lower() for m in _real_kv_hash_modes()],
             help=(
                 "Check the real KV-cache in the canary. "
                 "'none' (default) disables the feature. "

python/sglang/srt/utils/common.py

@@ -95,7 +95,6 @@ from starlette.routing import Mount
 from torch import nn
 from torch.library import Library
 from torch.utils._contextlib import _DecoratorContextManager
-from torchvision.io import decode_jpeg
 from typing_extensions import Literal
 
 from sglang.srt.environ import envs
@@ -266,8 +265,10 @@ def _check_cuda_device_version(
 ):
     if not is_cuda():
         return False
+    # get_device_sm() answers from NVML while torch.cuda is uninitialized, so
+    # the platform probes evaluated at import time do not create a CUDA context.
     return (
-        torch.cuda.get_device_capability()[0] in device_capability_majors
+        get_device_sm() // 10 in device_capability_majors
         and tuple(map(int, torch.version.cuda.split(".")[:2])) >= cuda_version
     )
 
@@ -582,6 +583,16 @@ def get_dispatch_device_backend():
 
 @lru_cache(maxsize=1)
 def get_device_module():
+    # Resolve from the platform checks: torch.get_device_module() with no
+    # argument initializes the CUDA runtime, which poisons fork() startup.
+    if is_cuda() or is_hip():
+        return torch.cuda
+    if is_npu():
+        return torch.npu
+    if is_xpu():
+        return torch.xpu
+    if is_musa():
+        return torch.musa
     return torch.get_device_module()
 
 
@@ -630,8 +641,55 @@ def get_amdgpu_memory_capacity():
         )
 
 
+def _get_device_sm_via_nvml() -> Optional[int]:
+    # Compute capability of torch device 0, read while torch.cuda stays
+    # uninitialized; None when NVML cannot answer and the caller falls back.
+    try:
+        import pynvml
+    except ImportError:
+        logger.debug("get_device_sm: pynvml is not installed, using torch.cuda")
+        return None
+    # Private torch API, read defensively: it maps the torch ordinal to the NVML
+    # index under CUDA_VISIBLE_DEVICES / MIG; absent or failing -> fall back.
+    getter = getattr(torch.cuda, "_get_nvml_device_index", None)
+    if getter is None:
+        logger.debug(
+            "get_device_sm: torch.cuda._get_nvml_device_index is missing, "
+            "using torch.cuda"
+        )
+        return None
+    try:
+        idx = getter(0)
+    except Exception:
+        logger.debug(
+            "get_device_sm: torch.cuda._get_nvml_device_index(0) failed, "
+            "using torch.cuda",
+            exc_info=True,
+        )
+        return None
+    try:
+        pynvml.nvmlInit()
+        try:
+            handle = pynvml.nvmlDeviceGetHandleByIndex(idx)
+            major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
+        finally:
+            pynvml.nvmlShutdown()
+        return major * 10 + minor
+    except Exception:
+        logger.debug(
+            "get_device_sm: NVML query failed, using torch.cuda", exc_info=True
+        )
+        return None
+
+
 def get_device_sm():
     if torch.cuda.is_available() or is_musa():
+        # Called at import time (e.g. by the DeepGEMM configurer): initializing
+        # torch.cuda here would create a context and poison fork() startup.
+        if not is_musa() and not torch.cuda.is_initialized():
+            sm = _get_device_sm_via_nvml()
+            if sm is not None:
+                return sm
         major, minor = torch.cuda.get_device_capability()
         return major * 10 + minor
     return 0
@@ -1859,6 +1917,8 @@ def _load_image(
                 )
 
                 return decode_jpeg_with_fancy_upsampling(image_bytes)
+            from torchvision.io import decode_jpeg  # lazy: ~1 s of torch._dynamo
+
             encoded_image = torch.frombuffer(image_bytes, dtype=torch.uint8)
             image_tensor = decode_jpeg(encoded_image, device="cuda")
             return image_tensor

python/sglang/srt/utils/hf_transformers_patches.py

@@ -23,8 +23,11 @@ all patches.  It is safe to import multiple times -- patches are idempotent.
 """
 
 import inspect
+import logging
 
-from sglang.srt.utils import logger
+# Plain logger: importing sglang.srt.utils here pulls torch/transformers/triton
+# into every `import sglang` (this module runs from sglang/__init__.py).
+logger = logging.getLogger(__name__)
 
 _applied = False