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sgl-project--sglang/python/sglang/srt/mem_cache/mmap_allocator.py
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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

128 lines
4.7 KiB
Python

import ctypes
import ctypes.util
import logging
import math
import mmap
import os
import weakref
import torch
from sglang.srt.environ import envs
logger = logging.getLogger(__name__)
# Load libc once at module level so munmap is callable safely at GC/shutdown time.
# Resolve the SONAME via find_library so the allocator also works on systems
# whose libc is not named "libc.so.6" (e.g. musl / Alpine).
try:
_libc_name = ctypes.util.find_library("c") or "libc.so.6"
_libc = ctypes.CDLL(_libc_name, use_errno=True)
_libc.mmap.restype = ctypes.c_void_p
_libc.mmap.argtypes = [
ctypes.c_void_p,
ctypes.c_size_t,
ctypes.c_int,
ctypes.c_int,
ctypes.c_int,
ctypes.c_long,
]
_libc.munmap.restype = ctypes.c_int
_libc.munmap.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
except OSError:
_libc = None
# MAP_POPULATE is in Python's mmap module only since 3.11.
_MAP_POPULATE = getattr(mmap, "MAP_POPULATE", 0x08000)
# MAP_HUGETLB and MAP_HUGE_* are Linux-specific and not in Python's mmap module.
_MAP_HUGETLB = 0x40000
_MAP_HUGE_2MB = 21 << 26 # 0x1400000
_MAP_HUGE_1GB = 30 << 26 # 0x78000000
_MAP_FAILED = ctypes.c_void_p(-1).value
def _alloc_hugepage(n_bytes: int, alloc_bytes: int, extra_flags: int) -> ctypes.Array:
"""Call mmap via libc with hugepage flags and return an owning ctypes array.
munmap fires automatically via weakref.finalize when the array is
garbage-collected (i.e. when the tensor that wraps it is freed).
"""
ptr = _libc.mmap(
None,
alloc_bytes,
mmap.PROT_READ | mmap.PROT_WRITE,
mmap.MAP_SHARED | mmap.MAP_ANONYMOUS | _MAP_POPULATE | extra_flags,
-1,
0,
)
if ptr is None or ptr == _MAP_FAILED:
errno = ctypes.get_errno()
raise OSError(errno, os.strerror(errno))
array = (ctypes.c_uint8 * n_bytes).from_address(ptr)
weakref.finalize(array, _libc.munmap, ctypes.c_void_p(ptr), alloc_bytes)
return array
def alloc_mmap(dims: tuple, dtype: torch.dtype) -> torch.Tensor:
"""Allocate a host tensor via anonymous mmap. Set SGLANG_HUGEPAGE_SIZE=2MB or 1GB for hugepages.
MAP_SHARED + MAP_POPULATE are both required so cudaHostRegister pins real,
pre-faulted physical pages (otherwise pinning can race with COW or page
faults and the device ends up reading stale data).
The tensor owns the mapping; munmap fires when the tensor is freed.
"""
# Re-read per call (not cached) so that envs.SGLANG_HUGEPAGE_SIZE.override()
# works correctly in tests.
hugepage_size = (envs.SGLANG_HUGEPAGE_SIZE.get() or "").strip().upper()
n_bytes = math.prod(dims) * torch.empty([], dtype=dtype).element_size()
if hugepage_size == "":
page_size, extra_flags = mmap.PAGESIZE, 0
elif hugepage_size == "2MB":
page_size, extra_flags = 2 * 1024 * 1024, _MAP_HUGETLB | _MAP_HUGE_2MB
elif hugepage_size == "1GB":
page_size, extra_flags = 1024 * 1024 * 1024, _MAP_HUGETLB | _MAP_HUGE_1GB
else:
logger.warning(
"Unrecognized SGLANG_HUGEPAGE_SIZE=%r; expected '2MB' or '1GB'. "
"Falling back to plain page-size mmap.",
envs.SGLANG_HUGEPAGE_SIZE.get(),
)
page_size, extra_flags = mmap.PAGESIZE, 0
alloc_bytes = math.ceil(n_bytes / page_size) * page_size
if extra_flags:
if _libc is None:
logger.error(
"Hugepage mmap requested but libc.so.6 could not be loaded; "
"falling back to plain mmap. SGLANG_HUGEPAGE_SIZE=%s will be ignored.",
hugepage_size,
)
else:
try:
array = _alloc_hugepage(n_bytes, alloc_bytes, extra_flags)
return torch.frombuffer(
array, dtype=dtype, count=math.prod(dims)
).reshape(dims)
except OSError as e:
logger.error(
"Hugepage mmap via libc failed (%s); falling back to plain mmap. "
"SGLANG_HUGEPAGE_SIZE=%s will be ignored.",
e,
hugepage_size,
)
alloc_bytes = math.ceil(n_bytes / mmap.PAGESIZE) * mmap.PAGESIZE
# Plain mmap path -- used directly when no hugepages requested, or as fallback.
# torch.frombuffer keeps a reference to mm inside the tensor storage, so mm
# stays alive until the tensor is freed and mmap.mmap.__del__ calls munmap.
mm = mmap.mmap(
-1,
alloc_bytes,
flags=mmap.MAP_SHARED | mmap.MAP_ANONYMOUS | _MAP_POPULATE,
prot=mmap.PROT_READ | mmap.PROT_WRITE,
)
return torch.frombuffer(mm, dtype=dtype, count=math.prod(dims)).reshape(dims)