vllm-omni/vllm_omni/platforms/cuda/platform.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import torch
import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.config.kernel import IrOpPriorityConfig
from vllm.logger import init_logger
from vllm.platforms.cuda import CudaPlatformBase
from vllm.platforms.interface import DeviceCapability

from vllm_omni.diffusion.attention.backends.registry import DiffusionAttentionBackendEnum
from vllm_omni.platforms.interface import OmniPlatform, OmniPlatformEnum

logger = init_logger(__name__)


class CudaOmniPlatform(OmniPlatform, CudaPlatformBase):
    """CUDA/GPU implementation of OmniPlatform (default).

    Inherits all CUDA-specific implementations from vLLM's CudaPlatform,
    and adds Omni-specific interfaces from OmniPlatform.
    """

    _omni_enum = OmniPlatformEnum.CUDA

    @classmethod
    def get_omni_ar_worker_cls(cls) -> str:
        return "vllm_omni.worker.gpu_ar_worker.GPUARWorker"

    @classmethod
    def get_omni_generation_worker_cls(cls) -> str:
        return "vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker"

    @classmethod
    def get_default_stage_config_path(cls) -> str:
        return "vllm_omni/model_executor/stage_configs"

    @classmethod
    def has_flash_attn_package(cls) -> bool:
        from vllm_omni.diffusion.attention.backends.utils.fa import is_flash_attn_installed

        # Turing/Tesla/T4 GPUs don't support flash attention well
        gpu_name = cls.get_device_name()
        if "Turing" in gpu_name or "Tesla" in gpu_name or "T4" in gpu_name:
            return False

        if not is_flash_attn_installed():
            return False

        return True

    @classmethod
    def get_diffusion_attn_backend_cls(
        cls,
        selected_backend: str | None,
        head_size: int,
    ) -> str:
        from vllm_omni.diffusion.envs import PACKAGES_CHECKER

        # Check compute capability for Flash Attention support
        # Flash Attention requires compute capability >= 8.0 and < 10.0
        compute_capability = cls.get_device_capability()
        compute_supported = False
        if compute_capability is not None:
            major, minor = compute_capability
            capability = major * 10 + minor
            compute_supported = 80 <= capability < 100

        # Check if FA packages are available
        packages_info = PACKAGES_CHECKER.get_packages_info()
        packages_available = packages_info.get("has_flash_attn", False)

        # Both compute capability and packages must be available for FA
        flash_attn_supported = compute_supported and packages_available

        if selected_backend is not None:
            backend_upper = selected_backend.upper()
            if backend_upper == "FLASH_ATTN" and not flash_attn_supported:
                if not compute_supported:
                    logger.warning(
                        "Flash Attention requires GPU with compute capability >= 8.0 "
                        "and < 10.0. Falling back to TORCH_SDPA backend."
                    )
                elif not packages_available:
                    logger.warning("Flash Attention packages not available. Falling back to TORCH_SDPA backend.")
                logger.debug("Defaulting to diffusion attention backend SDPA")
                return DiffusionAttentionBackendEnum.TORCH_SDPA.get_path()
            backend = DiffusionAttentionBackendEnum[backend_upper]
            logger.debug("Using diffusion attention backend '%s'", backend_upper)
            return backend.get_path()

        if flash_attn_supported:
            logger.debug("Defaulting to diffusion attention backend FLASH_ATTN")
            return DiffusionAttentionBackendEnum.FLASH_ATTN.get_path()

        logger.debug("Defaulting to diffusion attention backend SDPA")
        return DiffusionAttentionBackendEnum.TORCH_SDPA.get_path()

    @classmethod
    def supports_torch_inductor(cls) -> bool:
        return True

    @classmethod
    def get_torch_device(cls, local_rank: int | None = None) -> torch.device:
        if local_rank is None:
            return torch.device("cuda")
        return torch.device("cuda", local_rank)

    @classmethod
    def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
        major, minor = torch.cuda.get_device_capability(device_id)
        return DeviceCapability(major=major, minor=minor)

    @classmethod
    def get_device_count(cls) -> int:
        return torch.accelerator.device_count()

    @classmethod
    def get_device_version(cls) -> str | None:
        return torch.version.cuda

    @classmethod
    def synchronize(cls) -> None:
        torch.accelerator.synchronize()

    @classmethod
    def get_free_memory(cls, device: torch.device | None = None) -> int:
        free, _ = torch.cuda.mem_get_info(device)
        return free

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        return torch.cuda.get_device_name(device_id)

    @classmethod
    def get_default_ir_op_priority(cls, vllm_config: VllmConfig) -> IrOpPriorityConfig:
        """Copied from vllm/platforms/cuda/platform.py v0.20.0 with force using vllm_c kernels"""
        default = ["vllm_c", "native"]  # Originally using "native" here when compiling

        # Use oink if enabled for rms_norm
        # TODO(Laurawly/luka): remove this env var,
        #  users can just use IR op priority directly
        rms_norm = default
        if envs.VLLM_USE_OINK_OPS:
            rms_norm = ["oink"] + default

        return IrOpPriorityConfig.with_default(default, rms_norm=rms_norm)