99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from vllm.config import VllmConfig
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from vllm.config.kernel import IrOpPriorityConfig
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from vllm.logger import init_logger
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from vllm.platforms.xpu import XPUPlatform
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from vllm_omni.diffusion.attention.backends.registry import DiffusionAttentionBackendEnum
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from vllm_omni.platforms.interface import OmniPlatform, OmniPlatformEnum
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logger = init_logger(__name__)
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class XPUOmniPlatform(OmniPlatform, XPUPlatform):
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"""XPU/Intel GPU implementation of OmniPlatform.
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Inherits all XPU-specific implementations from vLLM's XPUPlatform,
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and adds Omni-specific interfaces from OmniPlatform.
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"""
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_omni_enum = OmniPlatformEnum.XPU
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@classmethod
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def get_omni_ar_worker_cls(cls) -> str:
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return "vllm_omni.platforms.xpu.worker.xpu_ar_worker.XPUARWorker"
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@classmethod
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def get_omni_generation_worker_cls(cls) -> str:
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return "vllm_omni.platforms.xpu.worker.xpu_generation_worker.XPUGenerationWorker"
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@classmethod
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def get_diffusion_attn_backend_cls(
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cls,
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selected_backend: str | None,
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head_size: int,
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) -> str:
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compute_capability = torch.xpu.get_device_capability()
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# Intel Max 1100 and 1550 will not support flash_attn currently
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flash_attn_supported = compute_capability["architecture"] not in [13136561920]
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if selected_backend is not None:
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backend_upper = selected_backend.upper()
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backend = DiffusionAttentionBackendEnum[backend_upper]
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logger.debug("Using diffusion attention backend '%s'", backend_upper)
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return backend.get_path()
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if flash_attn_supported:
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logger.debug("Defaulting to diffusion attention backend FLASH_ATTN")
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return DiffusionAttentionBackendEnum.FLASH_ATTN.get_path()
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logger.debug("Defaulting to diffusion attention backend SDPA")
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return DiffusionAttentionBackendEnum.TORCH_SDPA.get_path()
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@classmethod
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def supports_torch_inductor(cls) -> bool:
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return True
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@classmethod
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def get_default_stage_config_path(cls) -> str:
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return "vllm_omni/platforms/xpu/stage_configs"
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@classmethod
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def get_torch_device(cls, local_rank: int | None = None) -> torch.device:
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if local_rank is None:
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return torch.device("xpu")
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return torch.device("xpu", local_rank)
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@classmethod
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def get_device_count(cls) -> int:
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return torch.xpu.device_count()
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@classmethod
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def get_device_version(cls) -> str | None:
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# XPU does not have a version string like CUDA
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return None
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@classmethod
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def synchronize(cls) -> None:
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torch.xpu.synchronize()
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@classmethod
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def get_free_memory(cls, device: torch.device | None = None) -> int:
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free, _ = torch.xpu.mem_get_info(device)
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return free
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@classmethod
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def get_profiler_cls(cls) -> str:
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"""Return XPU-specific profiler that handles XPU events."""
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return "vllm_omni.platforms.xpu.profiler.XPUTorchProfilerWrapper"
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@classmethod
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def get_default_ir_op_priority(cls, vllm_config: VllmConfig) -> IrOpPriorityConfig:
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"""Copied from vllm/platforms/xpu/platform.py v0.20.0 with force using xpu_kernels kernels"""
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default = ["xpu_kernels", "native"] # Originally using "native" here when compiling
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return IrOpPriorityConfig.with_default(default)
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