124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any
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import torch
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from vllm.logger import init_logger
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from vllm_musa.platform import MUSAPlatformBase
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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 MUSAOmniPlatform(OmniPlatform, MUSAPlatformBase):
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"""MUSA/Moore Threads GPU implementation of OmniPlatform.
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Inherits all MUSA-specific implementations from vllm-musa's MUSAPlatformBase,
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and adds Omni-specific interfaces from OmniPlatform.
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"""
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_omni_enum = OmniPlatformEnum.MUSA
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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.musa.worker.musa_ar_worker.MUSAARWorker"
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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.musa.worker.musa_generation_worker.MUSAGenerationWorker"
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@classmethod
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def get_default_stage_config_path(cls) -> str:
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return "vllm_omni/model_executor/stage_configs"
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@classmethod
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def get_diffusion_model_impl_qualname(cls, op_name: str) -> str:
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# MUSA uses default implementations for diffusion ops
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if op_name == "hunyuan_fused_moe":
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return "vllm_omni.diffusion.models.hunyuan_image_3.hunyuan_fused_moe.HunyuanFusedMoEDefault"
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return super().get_diffusion_model_impl_qualname(op_name)
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@classmethod
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def prepare_diffusion_op_runtime(cls, op_name: str, **kwargs: Any) -> None:
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# MUSA uses default runtime preparation
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return None
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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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"""Get the diffusion attention backend class path for MUSA platform.
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MUSA currently supports SDPA (Scaled Dot Product Attention) as the
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primary backend. Flash Attention support may be added in future
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when MUSA-specific implementations are available.
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Args:
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selected_backend: User-selected backend name (e.g., "FLASH_ATTN",
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"TORCH_SDPA"). If None, uses platform default.
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head_size: Attention head size.
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Returns:
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Fully qualified class path of the selected backend.
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"""
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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.info("Using diffusion attention backend '%s'", backend_upper)
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return backend.get_path()
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# Default to SDPA for MUSA as it's the most compatible backend
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logger.info("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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"""MUSA supports torch.compile with inductor backend."""
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return True
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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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"""Get the torch device for MUSA platform.
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Args:
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local_rank: Optional local rank for multi-GPU setups.
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Returns:
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torch.device for MUSA GPU.
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"""
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if local_rank is None:
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return torch.device("musa")
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return torch.device("musa", local_rank)
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@classmethod
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def get_device_count(cls) -> int:
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"""Get the number of available MUSA devices."""
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return torch.musa.device_count()
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@classmethod
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def synchronize(cls) -> None:
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"""Synchronize all MUSA operations."""
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torch.musa.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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"""Get the free memory on the MUSA device.
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Args:
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device: Optional device to query. If None, uses current device.
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Returns:
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Free memory in bytes.
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"""
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free, _ = torch.musa.mem_get_info(device)
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return free
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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return torch.musa.get_device_name(device_id)
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