class XPUPlatform(Platform):
_enum = PlatformEnum.XPU
device_name: str = "xpu"
device_type: str = "xpu"
dispatch_key: str = "XPU"
# Intel XPU's device key is "GPU" for Ray.
# see https://github.com/ray-project/ray/blob/6a5eb5865eeb9ccf058a79b44f107e327e360673/python/ray/_private/accelerators/intel_gpu.py#L20 # noqa: E501
ray_device_key: str = "GPU"
device_control_env_var: str = "ONEAPI_DEVICE_SELECTOR"
@classmethod
def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
dtype: torch.dtype, kv_cache_dtype: Optional[str],
block_size: int, use_v1: bool,
use_mla: bool) -> str:
if selected_backend != _Backend.IPEX:
logger.info("Cannot use %s backend on XPU.", selected_backend)
use_v1 = envs.VLLM_USE_V1
if use_v1:
logger.info("Using Flash Attention backend on V1 engine.")
return "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"
else:
logger.info("Using IPEX attention backend.")
return "vllm.attention.backends.ipex_attn.IpexAttnBackend"
@classmethod
def get_device_capability(
cls,
device_id: int = 0,
) -> Optional[DeviceCapability]:
# capacity format differs from cuda's and will cause unexpected
# failure, so use None directly
return None
@classmethod
def get_device_name(cls, device_id: int = 0) -> str:
return torch.xpu.get_device_name(device_id)
@classmethod
def get_device_total_memory(cls, device_id: int = 0) -> int:
device_props = torch.xpu.get_device_properties(device_id)
return device_props.total_memory
@classmethod
def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
return True
@classmethod
def inference_mode(cls):
return torch.no_grad()
@classmethod
def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
cache_config = vllm_config.cache_config
# in V1(or with ipex chunked prefill) block_size is 64
if cache_config and cache_config.block_size is None:
if envs.VLLM_USE_V1:
cache_config.block_size = 64
else:
cache_config.block_size = 16
# Instances created using VllmConfig() typically have model_config as
# None by default. The modification involves adding a check to prevent
# potential null exceptions check and update model config.
if vllm_config.model_config is not None:
model_config = vllm_config.model_config
if model_config.dtype == torch.bfloat16:
bf16_supported = cls.device_support_bf16()
if not bf16_supported:
model_config.dtype = torch.float16
if not model_config.enforce_eager:
logger.warning(
"CUDA graph is not supported on XPU, fallback to the eager "
"mode.")
model_config.enforce_eager = True
if vllm_config.speculative_config is not None:
raise NotImplementedError(
"XPU does not support speculative decoding")
if vllm_config.device_config is not None:
assert vllm_config.device_config.device_type == "xpu"
# check and update parallel config
parallel_config = vllm_config.parallel_config
if envs.VLLM_USE_V1:
parallel_config.worker_cls =\
"vllm.v1.worker.xpu_worker.XPUWorker"
else:
parallel_config.worker_cls = "vllm.worker.xpu_worker.XPUWorker"
if parallel_config.distributed_executor_backend is None:
if parallel_config.world_size > 1:
parallel_config.distributed_executor_backend = "ray"
else:
parallel_config.distributed_executor_backend = "uni"
elif parallel_config.distributed_executor_backend == "mp":
# FIXME(kunshang):
# spawn needs calling `if __name__ == '__main__':``
# fork is not supported for xpu start new process.
if envs.VLLM_WORKER_MULTIPROC_METHOD != "spawn":
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
logger.warning(
"Please use spawn as start method if you want to use mp.")
elif parallel_config.distributed_executor_backend != "ray" and \
parallel_config.distributed_executor_backend != "uni":
logger.warning(
"%s is not supported on XPU, fallback to ray distributed"
" executor backend.",
parallel_config.distributed_executor_backend)
parallel_config.distributed_executor_backend = "ray"
if vllm_config.model_config and vllm_config.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.chunked_prefill_enabled = False
vllm_config.scheduler_config.max_num_batched_tokens = max(
vllm_config.scheduler_config.max_model_len,
DEFAULT_MAX_NUM_BATCHED_TOKENS)
@classmethod
def is_pin_memory_available(cls):
logger.warning("Pin memory is not supported on XPU.")
return False
@classmethod
def get_current_memory_usage(cls,
device: Optional[torch.types.Device] = None
) -> float:
torch.xpu.reset_peak_memory_stats(device)
return torch.xpu.max_memory_allocated(device)
@classmethod
def device_support_bf16(cls) -> bool:
device_name = cls.get_device_name().lower()
if cls.is_client_gpu_a770():
logger.warning("Intel Arc A770 have bfloat16 accuracy known issue,"
" fallback to float16")
return False
else:
logger.info(
"Device name %s supports bfloat16. Please file an issue "
"if you encounter any accuracy problems with bfloat16.",
device_name)
return True
@classmethod
def is_data_center_gpu(cls) -> bool:
device_name = cls.get_device_name().lower()
return device_name.count("data center gpu") > 0
@classmethod
def is_client_gpu_a770(cls) -> bool:
device_name = cls.get_device_name().lower()
return device_name.count("a770") > 0
@classmethod
def get_device_communicator_cls(cls) -> str:
return "vllm.distributed.device_communicators.xpu_communicator.XpuCommunicator" # noqa
@classmethod
def supports_v1(cls, model_config: ModelConfig) -> bool:
return True
@classmethod
def device_count(cls) -> int:
return torch.xpu.device_count()