439 lines
16 KiB
Python
439 lines
16 KiB
Python
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
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# SPDX-License-Identifier: Apache-2.0
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# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/platforms/cuda.py
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"""Code inside this file can safely assume cuda platform, e.g. importing
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pynvml. However, it should not initialize cuda context.
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"""
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import os
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from collections.abc import Callable
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from functools import lru_cache, wraps
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from typing import TypeVar
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import torch
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from typing_extensions import ParamSpec
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from sglang.multimodal_gen.runtime.platforms.interface import (
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AttentionBackendEnum,
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DeviceCapability,
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Platform,
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PlatformEnum,
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)
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from sglang.multimodal_gen.runtime.utils.common import is_blackwell
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from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
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from sglang.multimodal_gen.utils import import_pynvml
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logger = init_logger(__name__)
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_P = ParamSpec("_P")
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_R = TypeVar("_R")
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pynvml = import_pynvml() # type: ignore[no-untyped-call]
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# pytorch 2.5 uses cudnn sdpa by default, which will cause crash on some models
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# see https://github.com/huggingface/diffusers/issues/9704 for details
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torch.backends.cuda.enable_cudnn_sdp(False)
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def device_id_to_physical_device_id(device_id: int) -> int:
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if "CUDA_VISIBLE_DEVICES" in os.environ:
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device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
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if device_ids == [""]:
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msg = (
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"CUDA_VISIBLE_DEVICES is set to empty string, which means"
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" GPU support is disabled. If you are using ray, please unset"
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" the environment variable `CUDA_VISIBLE_DEVICES` inside the"
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" worker/actor. "
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"Check https://github.com/vllm-project/vllm/issues/8402 for"
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" more information."
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)
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raise RuntimeError(msg)
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physical_device_id = device_ids[device_id]
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return int(physical_device_id)
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else:
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return device_id
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def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
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@wraps(fn)
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def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
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pynvml.nvmlInit()
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try:
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return fn(*args, **kwargs)
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finally:
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pynvml.nvmlShutdown()
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return wrapper
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class CudaPlatformBase(Platform):
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_enum = PlatformEnum.CUDA
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device_name: str = "cuda"
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device_type: str = "cuda"
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dispatch_key: str = "CUDA"
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device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
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@classmethod
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def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
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raise NotImplementedError
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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raise NotImplementedError
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@classmethod
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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raise NotImplementedError
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@classmethod
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def is_async_output_supported(cls, enforce_eager: bool | None) -> bool:
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if enforce_eager:
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logger.warning(
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"To see benefits of async output processing, enable CUDA "
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"graph. Since, enforce-eager is enabled, async output "
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"processor cannot be used"
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)
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return False
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return True
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@classmethod
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def is_full_nvlink(cls, device_ids: list[int]) -> bool:
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raise NotImplementedError
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@classmethod
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def log_warnings(cls) -> None:
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pass
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@classmethod
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def get_current_memory_usage(
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cls, device: torch.types.Device | None = None
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) -> float:
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torch.cuda.reset_peak_memory_stats(device)
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return float(torch.cuda.max_memory_allocated(device))
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@classmethod
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def get_attn_backend_cls_str(
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cls,
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selected_backend: AttentionBackendEnum | None,
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head_size: int,
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dtype: torch.dtype,
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) -> str:
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# TODO(will): maybe come up with a more general interface for local attention
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# if distributed is False, we always try to use Flash attn
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if selected_backend == AttentionBackendEnum.SLIDING_TILE_ATTN:
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try:
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from st_attn import sliding_tile_attention # noqa: F401
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from sglang.multimodal_gen.runtime.layers.attention.backends.sliding_tile_attn import ( # noqa: F401
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SlidingTileAttentionBackend,
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)
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logger.info("Using Sliding Tile Attention backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.sliding_tile_attn.SlidingTileAttentionBackend"
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except ImportError as e:
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logger.error(
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"Failed to import Sliding Tile Attention backend: %s", str(e)
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)
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raise ImportError(
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"Sliding Tile Attention backend is not installed. "
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) from e
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elif selected_backend == AttentionBackendEnum.SAGE_ATTN:
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try:
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from sageattention import sageattn # noqa: F401
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from sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn import ( # noqa: F401
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SageAttentionBackend,
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)
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logger.info("Using Sage Attention backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn.SageAttentionBackend"
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except ImportError as e:
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logger.info(e)
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logger.info(
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"Sage Attention backend is not installed. Fall back to Flash Attention."
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)
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elif selected_backend == AttentionBackendEnum.SAGE_ATTN_THREE:
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try:
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from sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn3 import ( # noqa: F401
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SageAttention3Backend,
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)
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from sglang.multimodal_gen.runtime.layers.attention.backends.sageattn.api import ( # noqa: F401
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sageattn_blackwell,
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)
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logger.info("Using Sage Attention 3 backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.sage_attn3.SageAttention3Backend"
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except ImportError as e:
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logger.info(e)
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logger.info(
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"Sage Attention 3 backend is not installed. Fall back to Flash Attention."
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)
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elif selected_backend == AttentionBackendEnum.VIDEO_SPARSE_ATTN:
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try:
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from vsa import block_sparse_attn # noqa: F401
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from sglang.multimodal_gen.runtime.layers.attention.backends.video_sparse_attn import ( # noqa: F401
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VideoSparseAttentionBackend,
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)
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logger.info("Using Video Sparse Attention backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.video_sparse_attn.VideoSparseAttentionBackend"
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except ImportError as e:
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logger.error(
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"Failed to import Video Sparse Attention backend: %s", str(e)
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)
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raise ImportError(
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"Video Sparse Attention backend is not installed. "
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) from e
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elif selected_backend == AttentionBackendEnum.VMOBA_ATTN:
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try:
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from kernel.attn.vmoba_attn.vmoba import moba_attn_varlen # noqa: F401
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from sglang.multimodal_gen.runtime.layers.attention.backends.vmoba import ( # noqa: F401
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VMOBAAttentionBackend,
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)
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logger.info("Using Video MOBA Attention backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.vmoba.VMOBAAttentionBackend"
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except ImportError as e:
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logger.error(
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"Failed to import Video MoBA Attention backend: %s", str(e)
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)
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raise ImportError(
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"Video MoBA Attention backend is not installed. "
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) from e
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elif selected_backend == AttentionBackendEnum.AITER:
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logger.info("Using AITer backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.aiter.AITerBackend"
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elif selected_backend == AttentionBackendEnum.TORCH_SDPA:
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logger.info("Using Torch SDPA backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.sdpa.SDPABackend"
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elif selected_backend in [
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AttentionBackendEnum.FA,
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]:
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if is_blackwell():
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from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (
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set_fa_ver,
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)
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set_fa_ver(4)
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target_backend = AttentionBackendEnum.FA
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elif selected_backend:
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raise ValueError(f"Invalid attention backend for {cls.device_name}")
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else:
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if is_blackwell():
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from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import (
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set_fa_ver,
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)
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set_fa_ver(4)
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target_backend = AttentionBackendEnum.FA
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if not cls.has_device_capability(80):
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logger.info(
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"Cannot use FlashAttention backend for Volta and Turing " "GPUs."
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)
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target_backend = AttentionBackendEnum.TORCH_SDPA
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elif dtype not in (torch.float16, torch.bfloat16):
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logger.info(
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"Cannot use FlashAttention backend for dtype other than "
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"torch.float16 or torch.bfloat16."
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)
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target_backend = AttentionBackendEnum.TORCH_SDPA
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# FlashAttn is valid for the model, checking if the package is
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# installed.
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if target_backend == AttentionBackendEnum.FA:
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try:
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from sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn import ( # noqa: F401
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FlashAttentionBackend,
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)
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supported_sizes = FlashAttentionBackend.get_supported_head_sizes()
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if head_size not in supported_sizes:
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logger.info(
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"Cannot use FlashAttention backend for head size %d.",
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head_size,
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)
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target_backend = AttentionBackendEnum.TORCH_SDPA
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except ImportError:
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logger.info(
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"Cannot use FlashAttention backend because the "
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"flash_attn package is not found. "
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"Make sure that flash_attn was built and installed "
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"(on by default)."
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)
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target_backend = AttentionBackendEnum.TORCH_SDPA
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if target_backend == AttentionBackendEnum.TORCH_SDPA:
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logger.info("Using Torch SDPA backend.")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.sdpa.SDPABackend"
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logger.info("Using FlashAttention (FA3 for hopper, FA4 for blackwell) backend")
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return "sglang.multimodal_gen.runtime.layers.attention.backends.flash_attn.FlashAttentionBackend"
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@classmethod
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def get_device_communicator_cls(cls) -> str:
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return "sglang.multimodal_gen.runtime.distributed.device_communicators.cuda_communicator.CudaCommunicator" # noqa
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# NVML utils
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# Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
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# all the related functions work on real physical device ids.
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# the major benefit of using NVML is that it will not initialize CUDA
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class NvmlCudaPlatform(CudaPlatformBase):
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@classmethod
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@lru_cache(maxsize=8)
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@with_nvml_context
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def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
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try:
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physical_device_id = device_id_to_physical_device_id(device_id)
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handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
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major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
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return DeviceCapability(major=major, minor=minor)
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except RuntimeError:
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return None
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@classmethod
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@lru_cache(maxsize=8)
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@with_nvml_context
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def has_device_capability(
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cls,
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capability: tuple[int, int] | int,
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device_id: int = 0,
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) -> bool:
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try:
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return bool(super().has_device_capability(capability, device_id))
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except RuntimeError:
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return False
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@classmethod
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@lru_cache(maxsize=8)
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@with_nvml_context
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def get_device_name(cls, device_id: int = 0) -> str:
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physical_device_id = device_id_to_physical_device_id(device_id)
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return cls._get_physical_device_name(physical_device_id)
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@classmethod
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@lru_cache(maxsize=8)
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@with_nvml_context
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def get_device_uuid(cls, device_id: int = 0) -> str:
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physical_device_id = device_id_to_physical_device_id(device_id)
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handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
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return str(pynvml.nvmlDeviceGetUUID(handle))
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@classmethod
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@lru_cache(maxsize=8)
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@with_nvml_context
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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physical_device_id = device_id_to_physical_device_id(device_id)
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handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
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return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)
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@classmethod
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@with_nvml_context
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def is_full_nvlink(cls, physical_device_ids: list[int]) -> bool:
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"""
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query if the set of gpus are fully connected by nvlink (1 hop)
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"""
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handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in physical_device_ids]
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for i, handle in enumerate(handles):
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for j, peer_handle in enumerate(handles):
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if i < j:
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try:
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p2p_status = pynvml.nvmlDeviceGetP2PStatus(
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handle,
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peer_handle,
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pynvml.NVML_P2P_CAPS_INDEX_NVLINK,
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)
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if p2p_status != pynvml.NVML_P2P_STATUS_OK:
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return False
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except pynvml.NVMLError:
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logger.exception(
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"NVLink detection failed. This is normal if"
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" your machine has no NVLink equipped."
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)
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return False
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return True
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@classmethod
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def _get_physical_device_name(cls, device_id: int = 0) -> str:
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handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
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return str(pynvml.nvmlDeviceGetName(handle))
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@classmethod
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@with_nvml_context
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def log_warnings(cls) -> None:
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device_ids: int = pynvml.nvmlDeviceGetCount()
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if device_ids > 1:
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device_names = [cls._get_physical_device_name(i) for i in range(device_ids)]
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if (
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len(set(device_names)) > 1
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and os.environ.get("CUDA_DEVICE_ORDER") != "PCI_BUS_ID"
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):
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logger.warning(
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"Detected different devices in the system: %s. Please"
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" make sure to set `CUDA_DEVICE_ORDER=PCI_BUS_ID` to "
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"avoid unexpected behavior.",
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", ".join(device_names),
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)
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class NonNvmlCudaPlatform(CudaPlatformBase):
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@classmethod
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def get_device_capability(cls, device_id: int = 0) -> DeviceCapability:
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major, minor = torch.cuda.get_device_capability(device_id)
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return DeviceCapability(major=major, minor=minor)
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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return str(torch.cuda.get_device_name(device_id))
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@classmethod
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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device_props = torch.cuda.get_device_properties(device_id)
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return int(device_props.total_memory)
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@classmethod
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def is_full_nvlink(cls, physical_device_ids: list[int]) -> bool:
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logger.exception(
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"NVLink detection not possible, as context support was"
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" not found. Assuming no NVLink available."
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)
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return False
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# Autodetect either NVML-enabled or non-NVML platform
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# based on whether NVML is available.
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nvml_available = False
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try:
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try:
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pynvml.nvmlInit()
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nvml_available = True
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except Exception:
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# On Jetson, NVML is not supported.
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nvml_available = False
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finally:
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if nvml_available:
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pynvml.nvmlShutdown()
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CudaPlatform = NvmlCudaPlatform if nvml_available else NonNvmlCudaPlatform
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try:
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from sphinx.ext.autodoc.mock import _MockModule
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if not isinstance(pynvml, _MockModule):
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CudaPlatform.log_warnings()
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except ModuleNotFoundError:
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CudaPlatform.log_warnings()
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