289 lines
10 KiB
Python
289 lines
10 KiB
Python
# This file is a pure Python wrapper for the NCCL library.
|
|
# The main purpose is to use NCCL combined with CUDA graph.
|
|
# Before writing this script, we tried the following approach:
|
|
# 1. We tried to use `cupy`, it calls NCCL correctly, but `cupy` itself
|
|
# often gets stuck when initializing the NCCL communicator.
|
|
# 2. We tried to use `torch.distributed`, but `torch.distributed.all_reduce`
|
|
# contains many other potential cuda APIs, that are not allowed during
|
|
# capturing the CUDA graph. For further details, please check
|
|
# https://discuss.pytorch.org/t/pytorch-cudagraph-with-nccl-operation-failed/ .
|
|
#
|
|
# Another rejected idea is to write a C/C++ binding for NCCL. It is usually
|
|
# doable, but we often encounter issues related with nccl versions, and need
|
|
# to switch between different versions of NCCL. See
|
|
# https://github.com/NVIDIA/nccl/issues/1234 for more details.
|
|
# A C/C++ binding is not flexible enough to handle this. It requires
|
|
# recompilation of the code every time we want to switch between different
|
|
# versions. This current implementation, with a **pure** Python wrapper, is
|
|
# more flexible. We can easily switch between different versions of NCCL by
|
|
# changing the environment variable `VLLM_NCCL_SO_PATH`, or the `so_file`
|
|
# variable in the code.
|
|
|
|
import ctypes
|
|
import platform
|
|
from typing import Optional, Union
|
|
|
|
# ===================== import region =====================
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.distributed import ProcessGroup, ReduceOp
|
|
|
|
from vllm.distributed.parallel_state import get_cpu_world_group, get_local_rank
|
|
from vllm.logger import init_logger
|
|
from vllm.utils import find_nccl_library, nccl_integrity_check
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
so_file = find_nccl_library()
|
|
|
|
try:
|
|
# load the library in another process.
|
|
# if it core dumps, it will not crash the current process
|
|
nccl_integrity_check(so_file)
|
|
nccl = ctypes.CDLL(so_file)
|
|
except Exception as e:
|
|
logger.error(
|
|
"Failed to load NCCL library from %s ."
|
|
"It is expected if you are not running on NVIDIA/AMD GPUs."
|
|
"Otherwise, the nccl library might not exist, be corrupted "
|
|
"or it does not support the current platform %s."
|
|
"One solution is to download libnccl2 version 2.18 from "
|
|
"https://developer.download.nvidia.com/compute/cuda/repos/ "
|
|
"and extract the libnccl.so.2 file. If you already have the "
|
|
"library, please set the environment variable VLLM_NCCL_SO_PATH"
|
|
" to point to the correct nccl library path.", so_file,
|
|
platform.platform())
|
|
raise e
|
|
|
|
# === export types and functions from nccl to Python ===
|
|
# for the original nccl definition, please check
|
|
# https://github.com/NVIDIA/nccl/blob/master/src/nccl.h.in
|
|
|
|
ncclResult_t = ctypes.c_int
|
|
|
|
_c_ncclGetErrorString = nccl.ncclGetErrorString
|
|
_c_ncclGetErrorString.restype = ctypes.c_char_p
|
|
_c_ncclGetErrorString.argtypes = [ncclResult_t]
|
|
|
|
|
|
def NCCL_CHECK(result: ncclResult_t) -> None:
|
|
if result != 0:
|
|
error_str = _c_ncclGetErrorString(result)
|
|
error_str = error_str.decode("utf-8")
|
|
raise RuntimeError(f"NCCL error: {error_str}")
|
|
|
|
|
|
# equivalent to c declaration:
|
|
# ncclResult_t ncclGetVersion(int *version);
|
|
_c_ncclGetVersion = nccl.ncclGetVersion
|
|
_c_ncclGetVersion.restype = ctypes.c_int
|
|
_c_ncclGetVersion.argtypes = [ctypes.POINTER(ctypes.c_int)]
|
|
|
|
|
|
def ncclGetVersion() -> str:
|
|
version = ctypes.c_int()
|
|
NCCL_CHECK(_c_ncclGetVersion(ctypes.byref(version)))
|
|
# something like 21903 --> "2.19.3"
|
|
version_str = str(version.value)
|
|
major = version_str[0].lstrip("0")
|
|
minor = version_str[1:3].lstrip("0")
|
|
patch = version_str[3:].lstrip("0")
|
|
return f"{major}.{minor}.{patch}"
|
|
|
|
|
|
class NcclUniqueId(ctypes.Structure):
|
|
_fields_ = [("internal", ctypes.c_byte * 128)]
|
|
|
|
|
|
# equivalent to c declaration:
|
|
# ncclResult_t ncclGetUniqueId(ncclUniqueId* uniqueId);
|
|
_c_ncclGetUniqueId = nccl.ncclGetUniqueId
|
|
_c_ncclGetUniqueId.restype = ctypes.c_int
|
|
_c_ncclGetUniqueId.argtypes = [ctypes.POINTER(NcclUniqueId)]
|
|
|
|
|
|
def ncclGetUniqueId() -> NcclUniqueId:
|
|
unique_id = NcclUniqueId()
|
|
NCCL_CHECK(_c_ncclGetUniqueId(ctypes.byref(unique_id)))
|
|
return unique_id
|
|
|
|
|
|
# equivalent to c declaration:
|
|
# ncclResult_t ncclCommInitRank(
|
|
# ncclComm_t* comm, int nranks, ncclUniqueId commId, int rank);
|
|
# note that ncclComm_t is a pointer type, so the first argument
|
|
# is a pointer to a pointer
|
|
_c_ncclCommInitRank = nccl.ncclCommInitRank
|
|
_c_ncclCommInitRank.restype = ctypes.c_int
|
|
_c_ncclCommInitRank.argtypes = [
|
|
ctypes.POINTER(ctypes.c_void_p), ctypes.c_int, NcclUniqueId, ctypes.c_int
|
|
]
|
|
|
|
ncclDataType_t = ctypes.c_int
|
|
|
|
|
|
class ncclDataTypeEnum:
|
|
ncclInt8 = 0
|
|
ncclChar = 0
|
|
ncclUint8 = 1
|
|
ncclInt32 = 2
|
|
ncclInt = 2
|
|
ncclUint32 = 3
|
|
ncclInt64 = 4
|
|
ncclUint64 = 5
|
|
ncclFloat16 = 6
|
|
ncclHalf = 6
|
|
ncclFloat32 = 7
|
|
ncclFloat = 7
|
|
ncclFloat64 = 8
|
|
ncclDouble = 8
|
|
ncclBfloat16 = 9
|
|
ncclNumTypes = 10
|
|
|
|
@classmethod
|
|
def from_torch(cls, dtype: torch.dtype) -> int:
|
|
if dtype == torch.int8:
|
|
return cls.ncclInt8
|
|
if dtype == torch.uint8:
|
|
return cls.ncclUint8
|
|
if dtype == torch.int32:
|
|
return cls.ncclInt32
|
|
if dtype == torch.int64:
|
|
return cls.ncclInt64
|
|
if dtype == torch.float16:
|
|
return cls.ncclFloat16
|
|
if dtype == torch.float32:
|
|
return cls.ncclFloat32
|
|
if dtype == torch.float64:
|
|
return cls.ncclFloat64
|
|
if dtype == torch.bfloat16:
|
|
return cls.ncclBfloat16
|
|
raise ValueError(f"Unsupported dtype: {dtype}")
|
|
|
|
|
|
ncclRedOp_t = ctypes.c_int
|
|
|
|
|
|
class ncclRedOpTypeEnum:
|
|
ncclSum = 0
|
|
ncclProd = 1
|
|
ncclMax = 2
|
|
ncclMin = 3
|
|
ncclAvg = 4
|
|
ncclNumOps = 5
|
|
|
|
@classmethod
|
|
def from_torch(cls, op: ReduceOp) -> int:
|
|
if op == ReduceOp.SUM:
|
|
return cls.ncclSum
|
|
if op == ReduceOp.PRODUCT:
|
|
return cls.ncclProd
|
|
if op == ReduceOp.MAX:
|
|
return cls.ncclMax
|
|
if op == ReduceOp.MIN:
|
|
return cls.ncclMin
|
|
if op == ReduceOp.AVG:
|
|
return cls.ncclAvg
|
|
raise ValueError(f"Unsupported op: {op}")
|
|
|
|
|
|
# equivalent to c declaration:
|
|
# ncclResult_t ncclAllReduce(
|
|
# const void* sendbuff, void* recvbuff, size_t count,
|
|
# ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm,
|
|
# udaStream_t stream);
|
|
# note that cudaStream_t is a pointer type, so the last argument is a pointer
|
|
_c_ncclAllReduce = nccl.ncclAllReduce
|
|
_c_ncclAllReduce.restype = ctypes.c_int
|
|
_c_ncclAllReduce.argtypes = [
|
|
ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t, ncclRedOp_t,
|
|
ncclDataType_t, ctypes.c_void_p, ctypes.c_void_p
|
|
]
|
|
|
|
# equivalent to c declaration:
|
|
# ncclResult_t ncclCommDestroy(ncclComm_t comm);
|
|
_c_ncclCommDestroy = nccl.ncclCommDestroy
|
|
_c_ncclCommDestroy.restype = ctypes.c_int
|
|
_c_ncclCommDestroy.argtypes = [ctypes.c_void_p]
|
|
|
|
|
|
class NCCLCommunicator:
|
|
|
|
def __init__(
|
|
self,
|
|
group: Optional[ProcessGroup] = None,
|
|
device: Optional[Union[int, str, torch.device]] = None,
|
|
):
|
|
"""
|
|
Args:
|
|
group: the process group to work on. If None, it will use the
|
|
default process group.
|
|
device: the device to bind the NCCLCommunicator to. If None,
|
|
it will be bind to f"cuda:{local_rank}".
|
|
It is the caller's responsibility to make sure each communicator
|
|
is bind to a unique device.
|
|
"""
|
|
assert dist.is_initialized()
|
|
group = get_cpu_world_group() if group is None else group
|
|
assert dist.get_backend(group) != dist.Backend.NCCL, (
|
|
"NCCLCommunicator should be attached to a non-NCCL group.")
|
|
self.group = group
|
|
self.rank = dist.get_rank(group)
|
|
self.world_size = dist.get_world_size(group)
|
|
if self.rank == 0:
|
|
self.unique_id = ncclGetUniqueId()
|
|
else:
|
|
self.unique_id = NcclUniqueId()
|
|
tensor = torch.ByteTensor(list(self.unique_id.internal))
|
|
dist.broadcast(tensor, src=0, group=group)
|
|
byte_list = tensor.tolist()
|
|
for i, byte in enumerate(byte_list):
|
|
self.unique_id.internal[i] = byte
|
|
self.comm = ctypes.c_void_p()
|
|
if device is None:
|
|
local_rank = get_local_rank()
|
|
device = torch.device(f"cuda:{local_rank}")
|
|
elif isinstance(device, int):
|
|
device = torch.device(f"cuda:{device}")
|
|
elif isinstance(device, str):
|
|
device = torch.device(device)
|
|
# now `device` is a `torch.device` object
|
|
assert isinstance(device, torch.device)
|
|
self.device = device
|
|
# nccl communicator and stream will use this device
|
|
# `torch.cuda.device` is a context manager that changes the
|
|
# current cuda device to the specified one
|
|
with torch.cuda.device(device):
|
|
NCCL_CHECK(
|
|
_c_ncclCommInitRank(ctypes.byref(self.comm), self.world_size,
|
|
self.unique_id, self.rank))
|
|
self.stream = torch.cuda.Stream()
|
|
|
|
def all_reduce(self,
|
|
tensor: torch.Tensor,
|
|
op: ReduceOp = ReduceOp.SUM,
|
|
stream=None):
|
|
# nccl communicator created on a specific device
|
|
# will only work on tensors on the same device
|
|
# otherwise it will cause "illegal memory access"
|
|
assert tensor.device == self.device, (
|
|
f"this nccl communicator is created to work on {self.device}, "
|
|
f"but the input tensor is on {tensor.device}")
|
|
if stream is None:
|
|
stream = self.stream
|
|
NCCL_CHECK(
|
|
_c_ncclAllReduce(ctypes.c_void_p(tensor.data_ptr()),
|
|
ctypes.c_void_p(tensor.data_ptr()),
|
|
tensor.numel(),
|
|
ncclDataTypeEnum.from_torch(tensor.dtype),
|
|
ncclRedOpTypeEnum.from_torch(op), self.comm,
|
|
ctypes.c_void_p(stream.cuda_stream)))
|
|
|
|
def __del__(self):
|
|
# `dist` module might have been already destroyed
|
|
if hasattr(dist, 'destroy_process_group'):
|
|
dist.destroy_process_group()
|
|
# function might have been already destroyed
|
|
if _c_ncclCommDestroy is not None:
|
|
_c_ncclCommDestroy(self.comm)
|