EngineX-MetaX/enginex-c_series-vllm
8
1
Fork 1
Code Issues Pull Requests Actions Projects Releases Wiki Activity

[gpt-oss] Add gpt-oss bf16 support

Browse Source
This commit is contained in:
wangjing
2025-08-13 21:25:57 +08:00
parent 5d2e7edf78
commit 17ea2ec6aa
1232 changed files with 777 additions and 36 deletions
Download Patch File Download Diff File Expand all files Collapse all files

0
vllm/device_allocator/__init__.py Normal file
View File

281
vllm/device_allocator/cumem.py Normal file
View File

@@ -0,0 +1,281 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# cumem-based pytorch pluggable allocator to implement sleep mode.
# other approaches tried but failed:
# - cuda-python package binding
# - custom libcuda driver ctypes wrapper
# both of them failed because of cuda context mismatch.
# not sure why, they are created from a different context.
# the only successful approach is to call cuda driver API in C.
import dataclasses
import gc
import os
from contextlib import contextmanager
from typing import Any, Callable, Optional, Union
import torch
from vllm.utils import is_pin_memory_available
def find_loaded_library(lib_name) -> Optional[str]:
"""
According to according to https://man7.org/linux/man-pages/man5/proc_pid_maps.5.html,
the file `/proc/self/maps` contains the memory maps of the process, which includes the
shared libraries loaded by the process. We can use this file to find the path of the
a loaded library.
""" # noqa
found_line = None
with open("/proc/self/maps") as f:
for line in f:
if lib_name in line:
found_line = line
break
if found_line is None:
# the library is not loaded in the current process
return None
# if lib_name is libcudart, we need to match a line with:
# address /path/to/libcudart-hash.so.11.0
start = found_line.index("/")
path = found_line[start:].strip()
filename = path.split("/")[-1]
assert filename.rpartition(".so")[0].startswith(lib_name), \
f"Unexpected filename: {filename} for library {lib_name}"
return path
cumem_available = False
try:
from vllm.cumem_allocator import (init_module, python_create_and_map,
python_unmap_and_release)
from vllm.distributed.device_communicators.cuda_wrapper import (
CudaRTLibrary)
lib_name = find_loaded_library("cumem_allocator")
libcudart = CudaRTLibrary()
cumem_available = True
except ModuleNotFoundError:
# rocm platform does not support cumem allocator
init_module = None
python_create_and_map = None
python_unmap_and_release = None
CudaRTLibrary = None
lib_name = None
libcudart = None
# py_device, py_alignedSize, py_d_mem, py_p_memHandle
HandleType = tuple[int, int, int, int]
@dataclasses.dataclass
class AllocationData:
handle: HandleType
tag: str
cpu_backup_tensor: Optional[torch.Tensor] = None
def create_and_map(allocation_handle: HandleType) -> None:
python_create_and_map(*allocation_handle)
def unmap_and_release(allocation_handle: HandleType) -> None:
python_unmap_and_release(*allocation_handle)
def get_pluggable_allocator(
python_malloc_fn: Callable[[int],
int], python_free_func: Callable[[int, int],
None]
) -> torch.cuda.memory.CUDAPluggableAllocator:
init_module(python_malloc_fn, python_free_func)
new_alloc = torch.cuda.memory.CUDAPluggableAllocator(
lib_name, 'my_malloc', 'my_free')
return new_alloc
@contextmanager
def use_memory_pool_with_allocator(
python_malloc_fn: Callable[[int], int],
python_free_func: Callable[[int, int], None]) -> None:
new_alloc = get_pluggable_allocator(python_malloc_fn, python_free_func)
mem_pool = torch.cuda.memory.MemPool(new_alloc._allocator)
with torch.cuda.memory.use_mem_pool(mem_pool):
yield mem_pool, new_alloc
class CuMemAllocator:
"""
A singleton class that manages a memory pool for CUDA tensors.
The memory in this pool can be offloaded or discarded when the
allocator sleeps.
Inside the `use_memory_pool(tag)` context, all tensors created will
be allocated in the memory pool, and has the same tag as the
tag passed to the context.
When we call `sleep`, all tensors with the specified tag will be
offloaded to CPU memory, and the rest of the tensors will be discarded.
When we call `wake_up`, all tensors that are previously offloaded
will be loaded back to GPU memory, and the rest of the tensors will
have empty memory.
Why it needs to be a singleton?
When allocated tensors are garbage collected, PyTorch will call
the free callback, which will call the `python_free_callback` method.
The C-extension uses a global variable to store the function of an
instance of this class. If we create multiple instances of this class,
the global variable will be overwritten and the free callback will
not work as expected.
"""
instance: "CuMemAllocator" = None
default_tag: str = "default"
@staticmethod
def get_instance() -> "CuMemAllocator":
"""
CuMemAllocator is a singleton class.
We cannot call the constructor directly.
Call this method to get the instance.
"""
assert cumem_available, "cumem allocator is not available"
if CuMemAllocator.instance is None:
CuMemAllocator.instance = CuMemAllocator()
return CuMemAllocator.instance
def __init__(self):
conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
assert "expandable_segments:True" not in conf, \
("Expandable segments are not compatible with memory pool. "
"Please track https://github.com/pytorch/pytorch/issues/147851 "
"for the latest updates.")
self.pointer_to_data: dict[int, AllocationData] = {}
self.current_tag: str = CuMemAllocator.default_tag
self.allocator_and_pools: dict[str, Any] = {}
def python_malloc_callback(self, allocation_handle: HandleType) -> None:
"""
Internal method to store the allocation data
when memory is allocated in the memory pool."""
py_d_mem = allocation_handle[2]
self.pointer_to_data[py_d_mem] = AllocationData(
allocation_handle, self.current_tag)
return
def python_free_callback(self, ptr: int) -> HandleType:
"""
Internal method to look up the allocation data
when memory is freed in the memory pool."""
data = self.pointer_to_data.pop(ptr)
if data.cpu_backup_tensor is not None:
data.cpu_backup_tensor = None
return data.handle
def sleep(
self,
offload_tags: Optional[Union[tuple[str, ...],
str]] = None) -> None:
"""
Put the allocator in sleep mode.
All data in the memory allocation with the specified tag will be
offloaded to CPU memory, and others will be discarded.
:param offload_tags: The tags of the memory allocation that will be
offloaded. The rest of the memory allocation will be discarded.
"""
if offload_tags is None:
# by default, allocated tensors are offloaded
# when the allocator sleeps
offload_tags = (CuMemAllocator.default_tag, )
elif isinstance(offload_tags, str):
offload_tags = (offload_tags, )
assert isinstance(offload_tags, tuple)
for ptr, data in self.pointer_to_data.items():
handle = data.handle
if data.tag in offload_tags:
size_in_bytes = handle[1]
cpu_backup_tensor = torch.empty(
size_in_bytes,
dtype=torch.uint8,
device='cpu',
pin_memory=is_pin_memory_available())
cpu_ptr = cpu_backup_tensor.data_ptr()
libcudart.cudaMemcpy(cpu_ptr, ptr, size_in_bytes)
data.cpu_backup_tensor = cpu_backup_tensor
unmap_and_release(handle)
gc.collect()
torch.cuda.empty_cache()
def wake_up(self, tags: Optional[list[str]] = None) -> None:
"""
Wake up the allocator from sleep mode.
All data that is previously offloaded will be loaded back to GPU
memory, and the rest of the data will have empty memory.
:param tags: The tags of the memory allocation that will be loaded
back to GPU memory. If None, all memory allocation will be loaded
back to GPU memory.
"""
for ptr, data in self.pointer_to_data.items():
if tags is None or data.tag in tags:
handle = data.handle
create_and_map(handle)
if data.cpu_backup_tensor is not None:
cpu_backup_tensor = data.cpu_backup_tensor
if cpu_backup_tensor is not None:
size_in_bytes = cpu_backup_tensor.numel(
) * cpu_backup_tensor.element_size()
cpu_ptr = cpu_backup_tensor.data_ptr()
libcudart.cudaMemcpy(ptr, cpu_ptr, size_in_bytes)
data.cpu_backup_tensor = None
@contextmanager
def use_memory_pool(self, tag: Optional[str] = None):
"""
A context manager to use the memory pool.
All memory allocation created inside the context will be allocated
in the memory pool, and has the specified tag.
:param tag: The tag of the memory allocation. If None, the default tag
will be used.
"""
if tag is None:
tag = CuMemAllocator.default_tag
assert isinstance(tag, str)
old_tag = self.current_tag
self.current_tag = tag
with use_memory_pool_with_allocator(self.python_malloc_callback,
self.python_free_callback) as data:
# start to hit another PyTorch bug in PyTorch 2.6,
# possibly because of gc-related issue w.r.t. the allocator and
# the memory pool.
# to avoid the issue, we keep a reference of the data.
# see https://github.com/pytorch/pytorch/issues/146431 .
self.allocator_and_pools[tag] = data
yield
# PyTorch's bug, calling torch.cuda.empty_cache() will error
# when using pluggable allocator, see
# https://github.com/pytorch/pytorch/issues/145168 .
# if we have some memory allocated and then freed,
# the memory will not be released.
# right now it is fine, because we only use this allocator
# during weight loading and kv cache creation, where we only
# allocate memory.
# TODO: we need to find a way to release the memory,
# i.e. calling torch.cuda.empty_cache()
self.current_tag = old_tag
def get_current_usage(self) -> int:
"""
Get the total number of bytes allocated in the memory pool.
"""
sum_bytes: int = 0
for ptr, data in self.pointer_to_data.items():
handle = data.handle
sum_bytes += handle[1]
return sum_bytes