721 lines
26 KiB
Python
721 lines
26 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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 datetime import timedelta
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from functools import cache, wraps
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from typing import TYPE_CHECKING, TypeVar
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import torch
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from torch.distributed import PrefixStore, ProcessGroup
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from torch.distributed.distributed_c10d import is_nccl_available
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from typing_extensions import ParamSpec
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# import custom ops, trigger op registration
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# import vllm._C # noqa
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from vllm.logger import init_logger
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from vllm.utils.import_utils import import_pynvml
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from vllm.utils.torch_utils import cuda_device_count_stateless
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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from .interface import DeviceCapability, Platform, PlatformEnum
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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from vllm.config.cache import CacheDType
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from vllm.v1.attention.selector import AttentionSelectorConfig
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else:
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VllmConfig = None
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CacheDType = None
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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()
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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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@cache
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def _get_backend_priorities(
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use_mla: bool,
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device_capability: DeviceCapability,
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num_heads: int | None = None,
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) -> list[AttentionBackendEnum]:
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"""Get backend priorities with lazy import to avoid circular dependency."""
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if use_mla:
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if device_capability.major == 10:
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# Prefer FlashInfer at low head counts (FlashMLA uses padding)
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if num_heads is not None and num_heads <= 16:
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sparse_backends = [
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AttentionBackendEnum.FLASHINFER_MLA_SPARSE,
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AttentionBackendEnum.FLASHMLA_SPARSE,
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]
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else:
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sparse_backends = [
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AttentionBackendEnum.FLASHMLA_SPARSE,
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AttentionBackendEnum.FLASHINFER_MLA_SPARSE,
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]
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return [
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AttentionBackendEnum.FLASHINFER_MLA,
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AttentionBackendEnum.CUTLASS_MLA,
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AttentionBackendEnum.FLASH_ATTN_MLA,
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AttentionBackendEnum.FLASHMLA,
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AttentionBackendEnum.TRITON_MLA,
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*sparse_backends,
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]
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else:
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return [
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AttentionBackendEnum.FLASH_ATTN_MLA,
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AttentionBackendEnum.FLASHMLA,
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AttentionBackendEnum.FLASHINFER_MLA,
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AttentionBackendEnum.TRITON_MLA,
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AttentionBackendEnum.FLASHMLA_SPARSE,
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]
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else:
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if device_capability.major == 10:
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return [
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AttentionBackendEnum.FLASHINFER,
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AttentionBackendEnum.FLASH_ATTN,
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AttentionBackendEnum.TRITON_ATTN,
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AttentionBackendEnum.FLEX_ATTENTION,
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]
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else:
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return [
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AttentionBackendEnum.FLASH_ATTN,
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AttentionBackendEnum.FLASHINFER,
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AttentionBackendEnum.TRITON_ATTN,
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AttentionBackendEnum.FLEX_ATTENTION,
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]
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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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ray_device_key: str = "GPU"
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dist_backend: str = "nccl"
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device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
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ray_noset_device_env_vars: list[str] = [
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"RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES",
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]
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@property
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def supported_dtypes(self) -> list[torch.dtype]:
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if self.has_device_capability(80):
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# Ampere and Hopper or later NVIDIA GPUs.
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return [torch.bfloat16, torch.float16, torch.float32]
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if self.has_device_capability(60):
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# Pascal, Volta and Turing NVIDIA GPUs, BF16 is not supported
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return [torch.float16, torch.float32]
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# Kepler and Maxwell NVIDIA GPUs, only FP32 is supported,
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# though vLLM doesn't support these GPUs.
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return [torch.float32]
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@classmethod
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def set_device(cls, device: torch.device) -> None:
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"""
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Set the device for the current platform.
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"""
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torch.cuda.set_device(device)
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# With this trick we can force the device to be set eagerly
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# see https://github.com/pytorch/pytorch/issues/155668
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# for why and when it is needed
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_ = torch.zeros(1, device=device)
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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_fully_connected(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):
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pass
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@classmethod
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def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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parallel_config = vllm_config.parallel_config
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model_config = vllm_config.model_config
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if parallel_config.worker_cls == "auto":
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parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
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cache_config = vllm_config.cache_config
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if cache_config and cache_config.block_size is None:
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cache_config.block_size = 16
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# TODO(lucas): handle this more gracefully
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# Note: model_config may be None during testing
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# Note: block_size is initialized in
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# HybridAttentionMambaModelConfig.verify_and_update_config
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# for models with both attention and mamba,
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# and doesn't need to be reinitialized here
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if (
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model_config is not None
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and model_config.use_mla
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and cache_config.block_size is not None
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):
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use_sparse = hasattr(vllm_config.model_config.hf_config, "index_topk")
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# If `--attention-config.backend` is not set and we are using MLA,
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# then we default to FlashMLA backend for non-blackwell GPUs,
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# else we default to CutlassMLA. For each case, we force the
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# required block_size.
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use_flashmla = False
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use_cutlass_mla = False
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use_flashinfer_mla = False
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use_flashmla_sparse = False
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use_flashinfer_mla_sparse = False
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from vllm.v1.attention.ops.flashmla import is_flashmla_dense_supported
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if vllm_config.attention_config.backend is None:
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# Default case
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hf_text_config = model_config.hf_text_config
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qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1)
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if (
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cls.is_device_capability_family(100)
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and not use_sparse
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and qk_nope_head_dim == 128
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):
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# Blackwell => Force FlashInfer MLA (unless sparse, i.e. DSv3.2)
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# and only if qk_nope_head_dim == 128 (kernel constraint)
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use_flashinfer_mla = True
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# Set the backend in AttentionConfig so it's used during
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# backend selection
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vllm_config.attention_config.backend = (
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AttentionBackendEnum.FLASHINFER_MLA
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)
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elif cls.is_device_capability_family(100) and not use_sparse:
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# Fall back to CUTLASS_MLA as 2nd priority on Blackwell
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use_cutlass_mla = True
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elif is_flashmla_dense_supported()[0]:
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# Non-Blackwell with FlashMLA support
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use_flashmla = True
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else:
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# Fallback: will use Triton MLA or other compatible backend
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pass
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else:
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# Forced case
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backend = vllm_config.attention_config.backend
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use_flashmla = backend == AttentionBackendEnum.FLASHMLA
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use_cutlass_mla = backend == AttentionBackendEnum.CUTLASS_MLA
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use_flashinfer_mla = backend == AttentionBackendEnum.FLASHINFER_MLA
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use_flashmla_sparse = backend == AttentionBackendEnum.FLASHMLA_SPARSE
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use_flashinfer_mla_sparse = (
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backend == AttentionBackendEnum.FLASHINFER_MLA_SPARSE
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)
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if (
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use_flashmla
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and is_flashmla_dense_supported()[0]
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and cache_config.block_size % 64 != 0
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):
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cache_config.block_size = 64
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logger.info("Forcing kv cache block size to 64 for FlashMLA backend.")
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if use_cutlass_mla and cache_config.block_size % 128 != 0:
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cache_config.block_size = 128
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logger.info(
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"Forcing kv cache block size to 128 for CUTLASS_MLA backend."
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)
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if (
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use_flashinfer_mla
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and cache_config.block_size != 32
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and cache_config.block_size % 64 != 0
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):
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cache_config.block_size = 64
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logger.info(
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"Forcing kv cache block size to 64 for FlashInferMLA backend."
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)
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if use_sparse:
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if not (use_flashmla_sparse or use_flashinfer_mla_sparse):
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use_flashmla_sparse = True
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if use_flashmla_sparse and cache_config.block_size != 64:
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cache_config.block_size = 64
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logger.info(
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"Forcing kv cache block size to 64 for FlashMLASparse backend."
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)
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elif use_flashinfer_mla_sparse and cache_config.block_size not in (
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32,
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64,
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):
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cache_config.block_size = 64
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logger.info(
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"Forcing kv cache block size to 64 for FlashInferMLASparse "
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"backend."
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)
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scheduler_config = vllm_config.scheduler_config
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# Note: model_config may be None during testing
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if (
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model_config is not None
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and model_config.is_mm_prefix_lm
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and scheduler_config.is_multimodal_model
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and not scheduler_config.disable_chunked_mm_input
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):
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logger.warning(
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"Forcing --disable_chunked_mm_input for models "
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"with multimodal-bidirectional attention."
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)
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scheduler_config.disable_chunked_mm_input = True
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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.empty_cache()
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torch.cuda.reset_peak_memory_stats(device)
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return torch.cuda.max_memory_allocated(device)
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@classmethod
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def get_valid_backends(
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cls,
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device_capability: DeviceCapability,
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attn_selector_config: "AttentionSelectorConfig",
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num_heads: int | None = None,
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) -> tuple[
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list[tuple["AttentionBackendEnum", int]],
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dict["AttentionBackendEnum", list[str]],
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]:
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valid_backends_priorities = []
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invalid_reasons = {}
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backend_priorities = _get_backend_priorities(
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attn_selector_config.use_mla,
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device_capability,
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num_heads,
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)
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for priority, backend in enumerate(backend_priorities):
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try:
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backend_class = backend.get_class()
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invalid_reasons_i = backend_class.validate_configuration(
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device_capability=device_capability,
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**attn_selector_config._asdict(),
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)
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except ImportError:
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invalid_reasons_i = ["ImportError"]
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if invalid_reasons_i:
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invalid_reasons[backend] = invalid_reasons_i
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else:
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valid_backends_priorities.append((backend, priority))
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return valid_backends_priorities, invalid_reasons
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@classmethod
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def get_attn_backend_cls(
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cls,
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selected_backend: "AttentionBackendEnum",
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attn_selector_config: "AttentionSelectorConfig",
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num_heads: int | None = None,
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) -> str:
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device_capability = cls.get_device_capability()
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assert device_capability is not None
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attn_selector_config = attn_selector_config._replace(block_size=None)
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# First try checking just the selected backend, if there is one.
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if selected_backend is not None:
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try:
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backend_class = selected_backend.get_class()
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invalid_reasons = backend_class.validate_configuration(
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device_capability=device_capability,
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**attn_selector_config._asdict(),
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)
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except ImportError:
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invalid_reasons = ["ImportError"]
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if invalid_reasons:
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raise ValueError(
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f"Selected backend {selected_backend} is not valid for "
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f"this configuration. Reason: {invalid_reasons}"
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)
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else:
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logger.info("Using %s backend.", selected_backend)
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return selected_backend.get_path()
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# No selected backend or the selected backend is invalid,
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# so we try finding a valid backend.
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valid_backends_priorities, invalid_reasons = cls.get_valid_backends(
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device_capability=device_capability,
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attn_selector_config=attn_selector_config,
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num_heads=num_heads,
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)
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reasons_str = (
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"{"
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+ ", ".join(
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f"{backend.name}: [{', '.join(reasons)}]"
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for backend, reasons in invalid_reasons.items()
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)
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+ "}"
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)
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config_str = attn_selector_config.__repr__()
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logger.debug_once(
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f"Some attention backends are not valid for {cls.device_name} with "
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f"{config_str}. Reasons: {reasons_str}."
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)
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if len(valid_backends_priorities) == 0:
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raise ValueError(
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f"No valid attention backend found for {cls.device_name} "
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f"with {config_str}. Reasons: {reasons_str}."
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)
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# We have found some valid backends. Select the one with the
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# highest priority.
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sorted_indices = sorted(
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range(len(valid_backends_priorities)),
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key=lambda i: valid_backends_priorities[i][1],
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)
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selected_index = sorted_indices[0]
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selected_backend = valid_backends_priorities[selected_index][0]
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logger.info_once(
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"Using %s attention backend out of potential backends: %s.",
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selected_backend.name,
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"[" + ", ".join(f"'{b[0].name}'" for b in valid_backends_priorities) + "]",
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scope="local",
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)
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return selected_backend.get_path()
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@classmethod
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def get_supported_vit_attn_backends(cls) -> list["AttentionBackendEnum"]:
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return [
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AttentionBackendEnum.FLASH_ATTN,
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AttentionBackendEnum.TRITON_ATTN,
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AttentionBackendEnum.TORCH_SDPA,
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AttentionBackendEnum.FLASHINFER,
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]
|
|
|
|
@classmethod
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|
def get_vit_attn_backend(
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cls,
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head_size: int,
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dtype: torch.dtype,
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backend: "AttentionBackendEnum | None" = None,
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) -> "AttentionBackendEnum":
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if backend is not None:
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assert backend in cls.get_supported_vit_attn_backends(), (
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f"Backend {backend} is not supported for vit attention. "
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f"Supported backends are: {cls.get_supported_vit_attn_backends()}"
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)
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logger.info_once(f"Using backend {backend} for vit attention")
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return backend
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|
|
|
cc = cls.get_device_capability()
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|
for vit_attn_backend in cls.get_supported_vit_attn_backends():
|
|
if vit_attn_backend == AttentionBackendEnum.TORCH_SDPA:
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continue
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try:
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backend_class = vit_attn_backend.get_class()
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is_backend_supported = backend_class.supports_head_size(
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head_size
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) and backend_class.supports_dtype(dtype)
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if cc is not None:
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is_backend_supported = (
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is_backend_supported
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|
and backend_class.supports_compute_capability(cc)
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)
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if is_backend_supported:
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logger.info_once(
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f"Using backend {vit_attn_backend} for vit attention"
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)
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return vit_attn_backend
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|
except ImportError:
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pass
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|
|
return AttentionBackendEnum.TORCH_SDPA
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|
|
|
@classmethod
|
|
def get_punica_wrapper(cls) -> str:
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return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
|
|
|
|
@classmethod
|
|
def get_device_communicator_cls(cls) -> str:
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|
return (
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"vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator" # noqa
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)
|
|
|
|
@classmethod
|
|
def supports_fp8(cls) -> bool:
|
|
return cls.has_device_capability(89)
|
|
|
|
@classmethod
|
|
def use_custom_allreduce(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def opaque_attention_op(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_static_graph_wrapper_cls(cls) -> str:
|
|
return "vllm.compilation.cuda_graph.CUDAGraphWrapper"
|
|
|
|
@classmethod
|
|
def stateless_init_device_torch_dist_pg(
|
|
cls,
|
|
backend: str,
|
|
prefix_store: PrefixStore,
|
|
group_rank: int,
|
|
group_size: int,
|
|
timeout: timedelta,
|
|
) -> ProcessGroup:
|
|
assert is_nccl_available()
|
|
pg: ProcessGroup = ProcessGroup(
|
|
prefix_store,
|
|
group_rank,
|
|
group_size,
|
|
)
|
|
from torch.distributed.distributed_c10d import ProcessGroupNCCL
|
|
|
|
backend_options = ProcessGroupNCCL.Options()
|
|
backend_options._timeout = timeout
|
|
|
|
backend_class = ProcessGroupNCCL(
|
|
prefix_store, group_rank, group_size, backend_options
|
|
)
|
|
backend_type = ProcessGroup.BackendType.NCCL
|
|
device = torch.device("cuda")
|
|
pg._set_default_backend(backend_type)
|
|
backend_class._set_sequence_number_for_group()
|
|
|
|
pg._register_backend(device, backend_type, backend_class)
|
|
return pg
|
|
|
|
@classmethod
|
|
def device_count(cls) -> int:
|
|
return cuda_device_count_stateless()
|
|
|
|
@classmethod
|
|
def check_if_supports_dtype(cls, dtype: torch.dtype):
|
|
if dtype == torch.bfloat16: # noqa: SIM102
|
|
if not cls.has_device_capability(80):
|
|
capability = cls.get_device_capability()
|
|
gpu_name = cls.get_device_name()
|
|
|
|
if capability is None:
|
|
compute_str = "does not have a compute capability"
|
|
else:
|
|
version_str = capability.as_version_str()
|
|
compute_str = f"has compute capability {version_str}"
|
|
|
|
raise ValueError(
|
|
"Bfloat16 is only supported on GPUs "
|
|
"with compute capability of at least 8.0. "
|
|
f"Your {gpu_name} GPU {compute_str}. "
|
|
"You can use float16 instead by explicitly setting the "
|
|
"`dtype` flag in CLI, for example: --dtype=half."
|
|
)
|
|
|
|
@classmethod
|
|
def insert_blocks_to_device(
|
|
cls,
|
|
src_cache: torch.Tensor,
|
|
dst_cache: torch.Tensor,
|
|
src_block_indices: torch.Tensor,
|
|
dst_block_indices: torch.Tensor,
|
|
) -> None:
|
|
"""Copy blocks from src_cache to dst_cache on GPU."""
|
|
_src_cache = src_cache[:, src_block_indices]
|
|
dst_cache[:, dst_block_indices] = _src_cache.to(dst_cache.device)
|
|
|
|
@classmethod
|
|
def swap_out_blocks_to_host(
|
|
cls,
|
|
src_cache: torch.Tensor,
|
|
dst_cache: torch.Tensor,
|
|
src_block_indices: torch.Tensor,
|
|
dst_block_indices: torch.Tensor,
|
|
) -> None:
|
|
"""Copy blocks from GPU to host (CPU)."""
|
|
_src_cache = src_cache[:, src_block_indices]
|
|
dst_cache[:, dst_block_indices] = _src_cache.cpu()
|
|
|
|
@classmethod
|
|
def support_hybrid_kv_cache(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def support_static_graph_mode(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def num_compute_units(cls, device_id=0):
|
|
return torch.cuda.get_device_properties(device_id).multi_processor_count
|
|
|
|
|
|
# NVML utils
|
|
# Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
|
|
# all the related functions work on real physical device ids.
|
|
# the major benefit of using NVML is that it will not initialize CUDA
|
|
class NvmlCudaPlatform(CudaPlatformBase):
|
|
@classmethod
|
|
@cache
|
|
@with_nvml_context
|
|
def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
|
|
try:
|
|
physical_device_id = cls.device_id_to_physical_device_id(device_id)
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
|
|
major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
|
|
return DeviceCapability(major=9, minor=0)
|
|
except RuntimeError:
|
|
return None
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def has_device_capability(
|
|
cls,
|
|
capability: tuple[int, int] | int,
|
|
device_id: int = 0,
|
|
) -> bool:
|
|
try:
|
|
return super().has_device_capability(capability, device_id)
|
|
except RuntimeError:
|
|
return False
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def get_device_name(cls, device_id: int = 0) -> str:
|
|
physical_device_id = cls.device_id_to_physical_device_id(device_id)
|
|
return cls._get_physical_device_name(physical_device_id)
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def get_device_uuid(cls, device_id: int = 0) -> str:
|
|
physical_device_id = cls.device_id_to_physical_device_id(device_id)
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
|
|
return pynvml.nvmlDeviceGetUUID(handle)
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def get_device_total_memory(cls, device_id: int = 0) -> int:
|
|
physical_device_id = cls.device_id_to_physical_device_id(device_id)
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
|
|
return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
|
|
"""
|
|
query if the set of gpus are fully connected by nvlink (1 hop)
|
|
"""
|
|
handles = [pynvml.nvmlDeviceGetHandleByIndex(i) for i in physical_device_ids]
|
|
for i, handle in enumerate(handles):
|
|
for j, peer_handle in enumerate(handles):
|
|
if i < j:
|
|
try:
|
|
p2p_status = pynvml.nvmlDeviceGetP2PStatus(
|
|
handle,
|
|
peer_handle,
|
|
pynvml.NVML_P2P_CAPS_INDEX_NVLINK,
|
|
)
|
|
if p2p_status != pynvml.NVML_P2P_STATUS_OK:
|
|
return False
|
|
except pynvml.NVMLError:
|
|
logger.exception(
|
|
"NVLink detection failed. This is normal if"
|
|
" your machine has no NVLink equipped."
|
|
)
|
|
return False
|
|
return True
|
|
|
|
@classmethod
|
|
def _get_physical_device_name(cls, device_id: int = 0) -> str:
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
|
|
return pynvml.nvmlDeviceGetName(handle)
|
|
|
|
@classmethod
|
|
@with_nvml_context
|
|
def log_warnings(cls):
|
|
device_ids: int = pynvml.nvmlDeviceGetCount()
|
|
if device_ids > 1:
|
|
device_names = [cls._get_physical_device_name(i) for i in range(device_ids)]
|
|
if (
|
|
len(set(device_names)) > 1
|
|
and os.environ.get("CUDA_DEVICE_ORDER") != "PCI_BUS_ID"
|
|
):
|
|
logger.warning(
|
|
"Detected different devices in the system: %s. Please"
|
|
" make sure to set `CUDA_DEVICE_ORDER=PCI_BUS_ID` to "
|
|
"avoid unexpected behavior.",
|
|
", ".join(device_names),
|
|
)
|
|
|
|
|
|
class NonNvmlCudaPlatform(CudaPlatformBase):
|
|
@classmethod
|
|
@cache
|
|
def get_device_capability(cls, device_id: int = 0) -> DeviceCapability:
|
|
major, minor = torch.cuda.get_device_capability(device_id)
|
|
return DeviceCapability(major=9, minor=0)
|
|
|
|
@classmethod
|
|
def get_device_name(cls, device_id: int = 0) -> str:
|
|
return torch.cuda.get_device_name(device_id)
|
|
|
|
@classmethod
|
|
def get_device_total_memory(cls, device_id: int = 0) -> int:
|
|
device_props = torch.cuda.get_device_properties(device_id)
|
|
return device_props.total_memory
|
|
|
|
@classmethod
|
|
def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
|
|
logger.exception(
|
|
"NVLink detection not possible, as context support was"
|
|
" not found. Assuming no NVLink available."
|
|
)
|
|
return False
|
|
|
|
|
|
# Autodetect either NVML-enabled or non-NVML platform
|
|
# based on whether NVML is available.
|
|
nvml_available = False
|
|
try:
|
|
try:
|
|
pynvml.nvmlInit()
|
|
nvml_available = True
|
|
except Exception:
|
|
# On Jetson, NVML is not supported.
|
|
nvml_available = False
|
|
finally:
|
|
if nvml_available:
|
|
pynvml.nvmlShutdown()
|
|
|
|
CudaPlatform = NvmlCudaPlatform if nvml_available else NonNvmlCudaPlatform
|
|
|
|
CudaPlatform.log_warnings()
|