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import glob
import importlib.util
import logging
import os
import shutil
from pathlib import Path
from typing import List
import torch
logger = logging.getLogger(__name__)
def _get_compute_capability():
"""Get the compute capability of the current GPU."""
if not torch.cuda.is_available():
return None
# Get the current device
device = torch.cuda.current_device()
properties = torch.cuda.get_device_properties(device)
# Return as integer (major * 10 + minor)
return properties.major * 10 + properties.minor
def _filter_compiled_extensions(file_list):
"""Filter and prioritize compiled extensions over Python source files."""
compiled_extensions = [".so", ".pyd", ".dll"] # Common compiled extension suffixes
compiled_files = []
other_files = []
for file_path in file_list:
path = Path(file_path)
# Check if it's a compiled extension (including complex names like .abi3.so, .cpython-312.so)
if any(
str(path).endswith(ext) or ext in str(path) for ext in compiled_extensions
):
compiled_files.append(file_path)
else:
other_files.append(file_path)
# Return compiled files first, then others
return compiled_files + other_files
def _load_architecture_specific_ops():
"""Load the appropriate common_ops library based on GPU architecture."""
compute_capability = _get_compute_capability()
logger.debug(
f"[sgl_kernel] GPU Detection: compute_capability = {compute_capability}"
)
# Get the directory where sgl_kernel is installed
sgl_kernel_dir = Path(__file__).parent
logger.debug(f"[sgl_kernel] sgl_kernel directory: {sgl_kernel_dir}")
# Determine which version to load based on GPU architecture
if compute_capability == 90:
ops_subdir = "sm90"
variant_name = "SM90 (Hopper/H100 with fast math optimization)"
elif compute_capability is not None:
ops_subdir = "sm100"
variant_name = f"SM{compute_capability} (precise math for compatibility)"
else:
ops_subdir = "sm100"
variant_name = "CPU/No GPU detected (using precise math)"
# Look for the compiled module with any valid extension
ops_pattern = str(sgl_kernel_dir / ops_subdir / "common_ops.*")
raw_matching_files = glob.glob(ops_pattern)
matching_files = _filter_compiled_extensions(raw_matching_files)
logger.debug(f"[sgl_kernel] Attempting to load {variant_name}")
logger.debug(f"[sgl_kernel] Looking for library matching pattern: {ops_pattern}")
logger.debug(f"[sgl_kernel] Found files: {raw_matching_files}")
logger.debug(f"[sgl_kernel] Prioritized files: {matching_files}")
previous_import_errors: List[Exception] = []
# Try to load from the architecture-specific directory
if matching_files:
ops_path = Path(matching_files[0]) # Use the first prioritized file
logger.debug(f"[sgl_kernel] Found architecture-specific library: {ops_path}")
try:
# Load the module from specific path using importlib
spec = importlib.util.spec_from_file_location("common_ops", str(ops_path))
if spec is None:
raise ImportError(f"Could not create module spec for {ops_path}")
common_ops = importlib.util.module_from_spec(spec)
if spec.loader is None:
raise ImportError(f"Module spec has no loader for {ops_path}")
logger.debug(f"[sgl_kernel] Loading module from {ops_path}...")
spec.loader.exec_module(common_ops)
logger.debug(f"[sgl_kernel] ✓ Successfully loaded {variant_name}")
logger.debug(f"[sgl_kernel] ✓ Module file: {common_ops.__file__}")
return common_ops
except Exception as e:
previous_import_errors.append(e)
logger.debug(
f"[sgl_kernel] ✗ Failed to load from {ops_path}: {type(e).__name__}: {e}"
)
# Continue to fallback
else:
logger.debug(
f"[sgl_kernel] ✗ Architecture-specific library not found matching pattern: {ops_pattern}"
)
# Try alternative directory (in case installation structure differs)
alt_pattern = str(sgl_kernel_dir / "common_ops.*")
raw_alt_files = glob.glob(alt_pattern)
alt_matching_files = _filter_compiled_extensions(raw_alt_files)
logger.debug(f"[sgl_kernel] Attempting fallback: looking for pattern {alt_pattern}")
logger.debug(f"[sgl_kernel] Found fallback files: {raw_alt_files}")
logger.debug(f"[sgl_kernel] Prioritized fallback files: {alt_matching_files}")
if alt_matching_files:
alt_path = Path(alt_matching_files[0]) # Use the first prioritized file
logger.debug(f"[sgl_kernel] Found fallback library: {alt_path}")
try:
spec = importlib.util.spec_from_file_location("common_ops", str(alt_path))
if spec is None:
raise ImportError(f"Could not create module spec for {alt_path}")
common_ops = importlib.util.module_from_spec(spec)
if spec.loader is None:
raise ImportError(f"Module spec has no loader for {alt_path}")
logger.debug(f"[sgl_kernel] Loading fallback module from {alt_path}...")
spec.loader.exec_module(common_ops)
logger.debug(f"[sgl_kernel] ✓ Successfully loaded fallback library")
logger.debug(f"[sgl_kernel] ✓ Module file: {common_ops.__file__}")
return common_ops
except Exception as e:
previous_import_errors.append(e)
logger.debug(
f"[sgl_kernel] ✗ Failed to load fallback from {alt_path}: {type(e).__name__}: {e}"
)
else:
logger.debug(
f"[sgl_kernel] ✗ Fallback library not found matching pattern: {alt_pattern}"
)
# Final attempt: try standard Python import (for backward compatibility)
logger.debug(
f"[sgl_kernel] Final attempt: trying standard Python import 'common_ops'"
)
try:
import common_ops
logger.debug(f"[sgl_kernel] ✓ Successfully imported via standard Python import")
logger.debug(f"[sgl_kernel] ✓ Module file: {common_ops.__file__}")
return common_ops
except ImportError as e:
previous_import_errors.append(e)
logger.debug(f"[sgl_kernel] ✗ Standard Python import failed: {e}")
attempt_error_msg = "\n".join(
f"- {type(err).__name__}: {err}" for err in previous_import_errors
)
# All attempts failed
cuda_version = torch.version.cuda
if cuda_version and cuda_version.startswith("13"):
install_hint = (
"pip install sgl-kernel --index-url https://docs.sglang.ai/whl/cu130/"
)
else:
install_hint = "pip install --upgrade sgl_kernel"
error_msg = f"""
[sgl_kernel] CRITICAL: Could not load any common_ops library!
Attempted locations:
1. Architecture-specific pattern: {ops_pattern} - found files: {matching_files}
2. Fallback pattern: {alt_pattern} - found files: {alt_matching_files}
3. Standard Python import: common_ops - failed
GPU Info:
- Compute capability: {compute_capability}
- Expected variant: {variant_name}
- CUDA version: {cuda_version}
Please ensure sgl_kernel is properly installed with:
{install_hint}
Error details from previous import attempts:
{attempt_error_msg}
"""
logger.debug(error_msg)
raise ImportError(error_msg)
# copy & modify from torch/utils/cpp_extension.py
def _find_cuda_home():
"""Find the CUDA install path."""
# Guess #1
cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH")
if cuda_home is None:
# Guess #2
nvcc_path = shutil.which("nvcc")
if nvcc_path is not None:
cuda_home = os.path.dirname(os.path.dirname(nvcc_path))
else:
# Guess #3
cuda_home = "/usr/local/cuda"
return cuda_home
def _preload_cuda_library():
"""Preload the CUDA runtime library to help avoid 'libcudart.so not found' issues."""
cuda_home = Path(_find_cuda_home())
candidate_dirs = [
cuda_home / "lib",
cuda_home / "lib64",
Path("/usr/lib/x86_64-linux-gnu"),
Path("/usr/lib/aarch64-linux-gnu"),
Path("/usr/lib64"),
Path("/usr/lib"),
]
# Determine CUDA major version to try the matching library first.
# On CUDA 13 systems (e.g., DGX Spark), only libcudart.so.13 exists.
cuda_major = torch.version.cuda.split(".")[0] if torch.version.cuda else "12"
lib_versions = list(dict.fromkeys([cuda_major, "13", "12"]))
for base in candidate_dirs:
for lib_version in lib_versions:
candidate = base / f"libcudart.so.{lib_version}"
if candidate.exists():
try:
cuda_runtime_lib = candidate.resolve()
ctypes.CDLL(str(cuda_runtime_lib), mode=ctypes.RTLD_GLOBAL)
logger.debug(f"Preloaded CUDA runtime under {cuda_runtime_lib}")
return
except Exception as e:
logger.debug(f"Failed to load {candidate}: {e}")
continue
logger.debug("[sgl_kernel] Could not preload CUDA runtime library")
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