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Source code for lightning.pytorch.accelerators.cuda

# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import shutil
import subprocess
from typing import Any, Dict, List, Optional, Union

import torch
from typing_extensions import override

import lightning.pytorch as pl
from lightning.fabric.accelerators import _AcceleratorRegistry
from lightning.fabric.accelerators.cuda import _check_cuda_matmul_precision, _clear_cuda_memory, num_cuda_devices
from lightning.fabric.utilities.device_parser import _parse_gpu_ids
from lightning.fabric.utilities.types import _DEVICE
from lightning.pytorch.accelerators.accelerator import Accelerator
from lightning.pytorch.utilities.exceptions import MisconfigurationException

_log = logging.getLogger(__name__)


[docs]class CUDAAccelerator(Accelerator): """Accelerator for NVIDIA CUDA devices."""
[docs] @override def setup_device(self, device: torch.device) -> None: """ Raises: MisconfigurationException: If the selected device is not GPU. """ if device.type != "cuda": raise MisconfigurationException(f"Device should be GPU, got {device} instead") _check_cuda_matmul_precision(device) torch.cuda.set_device(device)
[docs] @override def setup(self, trainer: "pl.Trainer") -> None: # TODO refactor input from trainer to local_rank @four4fish self.set_nvidia_flags(trainer.local_rank) _clear_cuda_memory()
@staticmethod def set_nvidia_flags(local_rank: int) -> None: # set the correct cuda visible devices (using pci order) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" all_gpu_ids = ",".join(str(x) for x in range(num_cuda_devices())) devices = os.getenv("CUDA_VISIBLE_DEVICES", all_gpu_ids) _log.info(f"LOCAL_RANK: {local_rank} - CUDA_VISIBLE_DEVICES: [{devices}]")
[docs] @override def get_device_stats(self, device: _DEVICE) -> Dict[str, Any]: """Gets stats for the given GPU device. Args: device: GPU device for which to get stats Returns: A dictionary mapping the metrics to their values. Raises: FileNotFoundError: If nvidia-smi installation not found """ return torch.cuda.memory_stats(device)
[docs] @override def teardown(self) -> None: _clear_cuda_memory()
[docs] @staticmethod @override def parse_devices(devices: Union[int, str, List[int]]) -> Optional[List[int]]: """Accelerator device parsing logic.""" return _parse_gpu_ids(devices, include_cuda=True)
[docs] @staticmethod @override def get_parallel_devices(devices: List[int]) -> List[torch.device]: """Gets parallel devices for the Accelerator.""" return [torch.device("cuda", i) for i in devices]
[docs] @staticmethod @override def auto_device_count() -> int: """Get the devices when set to auto.""" return num_cuda_devices()
[docs] @staticmethod @override def is_available() -> bool: return num_cuda_devices() > 0
@classmethod @override def register_accelerators(cls, accelerator_registry: _AcceleratorRegistry) -> None: accelerator_registry.register( "cuda", cls, description=cls.__name__, )
def get_nvidia_gpu_stats(device: _DEVICE) -> Dict[str, float]: # pragma: no-cover """Get GPU stats including memory, fan speed, and temperature from nvidia-smi. Args: device: GPU device for which to get stats Returns: A dictionary mapping the metrics to their values. Raises: FileNotFoundError: If nvidia-smi installation not found """ nvidia_smi_path = shutil.which("nvidia-smi") if nvidia_smi_path is None: raise FileNotFoundError("nvidia-smi: command not found") gpu_stat_metrics = [ ("utilization.gpu", "%"), ("memory.used", "MB"), ("memory.free", "MB"), ("utilization.memory", "%"), ("fan.speed", "%"), ("temperature.gpu", "°C"), ("temperature.memory", "°C"), ] gpu_stat_keys = [k for k, _ in gpu_stat_metrics] gpu_query = ",".join(gpu_stat_keys) index = torch._utils._get_device_index(device) gpu_id = _get_gpu_id(index) result = subprocess.run( [nvidia_smi_path, f"--query-gpu={gpu_query}", "--format=csv,nounits,noheader", f"--id={gpu_id}"], encoding="utf-8", capture_output=True, check=True, ) def _to_float(x: str) -> float: try: return float(x) except ValueError: return 0.0 s = result.stdout.strip() stats = [_to_float(x) for x in s.split(", ")] return {f"{x} ({unit})": stat for (x, unit), stat in zip(gpu_stat_metrics, stats)} def _get_gpu_id(device_id: int) -> str: """Get the unmasked real GPU IDs.""" # All devices if `CUDA_VISIBLE_DEVICES` unset default = ",".join(str(i) for i in range(num_cuda_devices())) cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES", default=default).split(",") return cuda_visible_devices[device_id].strip()