class CpuPlatform(Platform):
_enum = PlatformEnum.CPU
device_name: str = "cpu"
device_type: str = "cpu"
dispatch_key: str = "CPU"
dist_backend: str = "gloo"
device_control_env_var = "CPU_VISIBLE_MEMORY_NODES"
@property
def supported_dtypes(self) -> list[torch.dtype]:
if self.get_cpu_architecture() == CpuArchEnum.POWERPC:
return [torch.bfloat16, torch.float32]
elif self.get_cpu_architecture() == CpuArchEnum.ARM and sys.platform.startswith(
"darwin"
):
if (
subprocess.check_output(
["sysctl -n hw.optional.arm.FEAT_BF16"], shell=True
).strip()
== b"1"
):
return [torch.bfloat16, torch.float16, torch.float32]
return [torch.float16, torch.float32]
elif self.get_cpu_architecture() == CpuArchEnum.RISCV:
# Workaround for Issue #25655: RISC-V scheduler bug with float16
#
# Background:
# - RISC-V currently uses scalar code path
# - There is a latent bug in the vLLM scheduler that provides
# invalid
# physical_block_idx values under certain conditions
# - This bug causes segmentation faults when using float16
# dtype on RISC-V
# - Testing shows that forcing float32 successfully bypasses
# this issue
#
# Technical details:
# - The bug manifests as out-of-bounds physical_block_idx in
# block_tables
# - Only occurs on RISC-V hardware
# tested on Sophgo SG2044
# - Does not reproduce on x86 or other architectures
# - Root cause is in Python-level scheduling logic,
# not C++ kernels
#
# This is a temporary workaround until the scheduler bug is fixed.
# See: https://github.com/vllm-project/vllm/issues/25655
return [torch.float32]
# x86/aarch64 CPU has supported both bf16 and fp16 natively.
return [torch.bfloat16, torch.float16, torch.float32]
@classmethod
def get_device_name(cls, device_id: int = 0) -> str:
return "cpu"
@classmethod
def get_attn_backend_cls(
cls,
selected_backend: "AttentionBackendEnum",
attn_selector_config: "AttentionSelectorConfig",
) -> str:
if selected_backend and selected_backend != AttentionBackendEnum.CPU_ATTN:
logger.info("Cannot use %s backend on CPU.", selected_backend)
if attn_selector_config.use_mla:
raise NotImplementedError("MLA is not supported on CPU.")
if attn_selector_config.use_sparse:
raise NotImplementedError("Sparse Attention is not supported on CPU.")
return AttentionBackendEnum.CPU_ATTN.get_path()
@classmethod
def get_device_total_memory(cls, device_id: int = 0) -> int:
from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import format_gib
kv_cache_space = envs.VLLM_CPU_KVCACHE_SPACE
node_dir = "/sys/devices/system/node"
if kv_cache_space is None:
nodes = (
[d for d in os.listdir(node_dir) if d.startswith("node")]
if os.path.exists(node_dir)
else []
)
num_numa_nodes = len(nodes) or 1
free_cpu_memory = psutil.virtual_memory().total // num_numa_nodes
DEFAULT_CPU_MEM_UTILIZATION = 0.5
kv_cache_space = int(free_cpu_memory * DEFAULT_CPU_MEM_UTILIZATION)
logger.warning_once(
"VLLM_CPU_KVCACHE_SPACE not set. Using %s GiB for KV cache.",
format_gib(kv_cache_space),
)
else:
kv_cache_space *= GiB_bytes
return kv_cache_space
@classmethod
def set_device(cls, device: torch.device) -> None:
"""
Set the device for the current platform.
"""
torch.cpu.set_device(device)
@classmethod
def inference_mode(cls):
return torch.no_grad()
@classmethod
def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
model_config = vllm_config.model_config
if model_config is not None:
model_config.disable_cascade_attn = True
cache_config = vllm_config.cache_config
if cache_config.block_size is None:
cache_config.block_size = 128
if cache_config.block_size % 32 != 0:
logger.warning(
"CPU backend prefers block_size is multiples of 32, "
"otherwise the performance is not optimized."
)
scheduler_config = vllm_config.scheduler_config
# async scheduling is not required on CPU
scheduler_config.async_scheduling = False
if (
scheduler_config.enable_chunked_prefill
or cache_config.enable_prefix_caching
) and is_quantized_kv_cache(cache_config.cache_dtype):
raise RuntimeError(
"Chunked-prefill and prefix-cache on the CPU "
"backend is not compatible with FP8 KV cache."
)
if cache_config.cache_dtype.startswith("fp8"):
logger.warning(
"CPU backend doesn't support KV cache quantization fallback to auto."
)
cache_config.cache_dtype = "auto"
cache_config.cpu_kvcache_space_bytes = CpuPlatform.get_device_total_memory()
# reserve at least one core for nixl_connector under p/d case
if vllm_config.kv_transfer_config and (
envs.VLLM_CPU_NUM_OF_RESERVED_CPU == 0
or envs.VLLM_CPU_NUM_OF_RESERVED_CPU is None
):
os.environ["VLLM_CPU_NUM_OF_RESERVED_CPU"] = "1"
parallel_config = vllm_config.parallel_config
if (
parallel_config.world_size > 1
and parallel_config.distributed_executor_backend is not None
and parallel_config.distributed_executor_backend != "mp"
):
logger.warning(
(
"%s is not supported on CPU, fallback to mp "
"distributed executor backend."
),
parallel_config.distributed_executor_backend,
)
parallel_config.distributed_executor_backend = "mp"
if parallel_config.worker_cls == "auto":
parallel_config.worker_cls = "vllm.v1.worker.cpu_worker.CPUWorker"
# Disable DBO
if parallel_config.enable_dbo:
logger.warning("Dual-Batch Overlap is not supported on CPU, disabled.")
parallel_config.enable_dbo = False
# Note: workaround for v1 gpu_model_runner
from vllm.config import CompilationMode
vllm_config.compilation_config.cudagraph_capture_sizes = []
compilation_config = vllm_config.compilation_config
if vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE:
# Note: vLLM V1 is using PIECEWISE level compilation, which will
# take time to compile kernels just-in-time with the inductor
# backend. For CPU CI tests, most of them are executed fast and
# compilations consume too much time, even with torch compile
# cache. So use VLLM_CPU_CI_ENV to indicate the CI environment,
# and just execute model with dynamo + eager mode to save time.
# VLLM_CPU_CI_ENV is only used as an internal variable.
if os.environ.get("VLLM_CPU_CI_ENV", "0") != "0":
backend = "eager"
else:
backend = "inductor"
compilation_config.mode = CompilationMode.DYNAMO_TRACE_ONCE
compilation_config.backend = backend
compilation_config.inductor_compile_config.update(
{
"dce": True,
"size_asserts": False,
"nan_asserts": False,
"epilogue_fusion": True,
}
)
if vllm_config.lora_config is not None:
compilation_config.mode = CompilationMode.NONE
assert vllm_config.device_config.device_type == "cpu"
#
# Environment variables for CPU executor
#
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
# Note: to avoid the error 'nthreads cannot be larger than environment
# variable "NUMEXPR_MAX_THREADS" (64)'.
os.environ["NUMEXPR_MAX_THREADS"] = str(get_max_threads())
if envs.VLLM_CPU_OMP_THREADS_BIND != "nobind":
# Set default threads num for OpenMP parallel
os.environ["OMP_NUM_THREADS"] = str(torch.get_num_threads())
else:
# In this case, setting the OpenMP configuration via
# OMP_NUM_THREADS is up to the user.
logger.info("Disabling binding processes to CPU cores...")
# Disable torch async compiling which won't work with daemonic processes
os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1"
# Disable multi-stream for shared experts as no Stream on CPU
os.environ["VLLM_DISABLE_SHARED_EXPERTS_STREAM"] = "1"
# Intel OpenMP setting
ld_preload_str = os.getenv("LD_PRELOAD", "")
if "libiomp5.so" in ld_preload_str:
# The time(milliseconds) that a thread should wait after
# completing the execution of a parallel region, before sleeping.
os.environ["KMP_BLOCKTIME"] = "1"
# Prevents the CPU to run into low performance state
os.environ["KMP_TPAUSE"] = "0"
# Provides fine granularity parallelism
os.environ["KMP_FORKJOIN_BARRIER_PATTERN"] = "dist,dist"
os.environ["KMP_PLAIN_BARRIER_PATTERN"] = "dist,dist"
os.environ["KMP_REDUCTION_BARRIER_PATTERN"] = "dist,dist"
if (
platform.system() == "Linux"
and Platform.get_cpu_architecture()
in (CpuArchEnum.ARM, CpuArchEnum.POWERPC)
and not ("libomp" in ld_preload_str or "libgomp" in ld_preload_str)
):
# We need to LD_PRELOAD PyTorch's libgomp, otherwise only
# one core will be properly utilized when we thread-bind
# See: https://github.com/vllm-project/vllm/issues/27369
# TODO: Remove once:
# https://github.com/pytorch/pytorch/issues/166087 is fixed
# We need to find the location of PyTorch's libgomp
torch_pkg = os.path.dirname(torch.__file__)
site_root = os.path.dirname(torch_pkg)
# Search both torch.libs and torch/lib - See: https://github.com/vllm-project/vllm/issues/30470
torch_libs_paths = [
os.path.join(site_root, "torch.libs"),
os.path.join(torch_pkg, "lib"),
]
pytorch_libgomp_so_candidates = []
for torch_libs in torch_libs_paths:
pytorch_libgomp_so_candidates.extend(
glob.glob(os.path.join(torch_libs, "libgomp*.so*"))
)
if pytorch_libgomp_so_candidates:
pytorch_libgomp_so = pytorch_libgomp_so_candidates[0]
if ld_preload_str:
ld_preload_str += ":"
ld_preload_str += pytorch_libgomp_so
os.environ["LD_PRELOAD"] = ld_preload_str
os.environ["LOCAL_WORLD_SIZE"] = str(
vllm_config.parallel_config.tensor_parallel_size
)
if model_config is not None and model_config.use_mla:
logger.info(
"MLA is enabled on a non-GPU platform; forcing chunked "
"prefill and prefix caching to be disabled."
)
vllm_config.scheduler_config.enable_chunked_prefill = False
vllm_config.scheduler_config.max_num_batched_tokens = max(
vllm_config.model_config.max_model_len,
vllm_config.scheduler_config.DEFAULT_MAX_NUM_BATCHED_TOKENS,
)
@classmethod
def get_allowed_cpu_core_node_list(cls) -> tuple[list[int], list[LogicalCPUInfo]]:
assert platform.system() == "Linux"
# Init LogicalCPUInfo from lscpu
lscpu_output = subprocess.check_output(
"lscpu -J -e=CPU,CORE,NODE", shell=True, text=True
)
lscpu_output = re.sub(r'"node":\s*-\s*(,|\n)', r'"node": 0\1', lscpu_output)
logical_cpu_list: list[LogicalCPUInfo] = json.loads(
lscpu_output, object_hook=LogicalCPUInfo.json_decoder
)["cpus"]
# Filter CPUs with invalid attributes
logical_cpu_list = [
x
for x in logical_cpu_list
if -1 not in (x.id, x.physical_core, x.numa_node)
]
# Filter allowed CPUs
if hasattr(os, "sched_getaffinity"):
allowed_cpu_id_list = os.sched_getaffinity(0)
else:
raise NotImplementedError("Unsupported OS")
logical_cpu_list = [x for x in logical_cpu_list if x.id in allowed_cpu_id_list]
# Get allowed NUMA nodes
allowed_numa_nodes = set()
for x in logical_cpu_list:
allowed_numa_nodes.add(x.numa_node) # type: ignore
allowed_numa_nodes_list = sorted(allowed_numa_nodes)
env_key = CpuPlatform.device_control_env_var
if env_key in os.environ and os.environ[env_key] != "":
visible_nodes = [int(s) for s in os.environ[env_key].split(",")]
allowed_numa_nodes_list = [
x for x in sorted(list(set(visible_nodes))) if x in allowed_numa_nodes
]
return allowed_numa_nodes_list, logical_cpu_list
@classmethod
def discover_numa_topology(cls) -> list[list[int]]:
"""
Discover NUMA topology and keep the last physical core of each numa
into one core group list for nixl start_kv_load()
"""
SYS_NODE = "/sys/devices/system/node"
SYS_CPU = "/sys/devices/system/cpu"
if not (os.path.exists(SYS_NODE) and os.path.exists(SYS_CPU)):
return []
core_rsv_for_kv = []
for node in os.listdir(SYS_NODE):
if not node.startswith("node") or not node[4:].isdigit():
continue
node_path = f"{SYS_NODE}/{node}"
seen_phys = set()
for cpu in os.listdir(node_path):
if not cpu.startswith("cpu") or not cpu[3:].isdigit():
continue
cpu_id = int(cpu[3:])
# thread_siblings based on cpu_id
path = f"{SYS_CPU}/cpu{cpu_id}/topology/thread_siblings_list"
if os.path.exists(path):
try:
with open(path) as f:
s = f.read()
cpus: list[int] = []
for part in s.strip().split(","):
if "-" in part:
a, b = map(int, part.split("-"))
cpus.extend(range(a, b + 1))
else:
cpus.append(int(part))
siblings = cpus if cpus else [cpu_id]
except (OSError, ValueError):
siblings = [cpu_id]
else:
siblings = [cpu_id]
phys = min(siblings)
if phys not in seen_phys:
seen_phys.add(phys)
if len(seen_phys) > 0:
core_rsv_for_kv.append(list(seen_phys))
return core_rsv_for_kv
@classmethod
def is_pin_memory_available(cls) -> bool:
return False
@classmethod
def get_punica_wrapper(cls) -> str:
return "vllm.lora.punica_wrapper.punica_cpu.PunicaWrapperCPU"
@classmethod
def get_device_communicator_cls(cls) -> str:
"""
Get device specific communicator class for distributed communication.
"""
return "vllm.distributed.device_communicators.cpu_communicator.CpuCommunicator" # noqa
@classmethod
def supports_structured_output(cls) -> bool:
return True
@classmethod
def opaque_attention_op(cls) -> bool:
return True
@classmethod
def support_hybrid_kv_cache(cls) -> bool:
return True