Source code for pytorch_lightning.accelerators.cuda
# Copyright The PyTorch Lightning 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import logging
import os
import shutil
import subprocess
from typing import Any, Dict, List, Optional, Union
import torch
import pytorch_lightning as pl
from lightning_fabric.accelerators.cuda import _check_cuda_matmul_precision, num_cuda_devices
from lightning_fabric.utilities.device_parser import _parse_gpu_ids
from lightning_fabric.utilities.types import _DEVICE
from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.utilities.exceptions import MisconfigurationException
_log = logging.getLogger(__name__)
[docs]class CUDAAccelerator(Accelerator):
"""Accelerator for NVIDIA CUDA devices."""
[docs] 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] def setup(self, trainer: "pl.Trainer") -> None:
# TODO refactor input from trainer to local_rank @four4fish
self.set_nvidia_flags(trainer.local_rank)
# clear cache before training
torch.cuda.empty_cache()
@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] 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] @staticmethod
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
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
def auto_device_count() -> int:
"""Get the devices when set to auto."""
return num_cuda_devices()
@classmethod
def register_accelerators(cls, accelerator_registry: Dict) -> None:
accelerator_registry.register(
"cuda",
cls,
description=f"{cls.__class__.__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(", ")]
gpu_stats = {f"{x} ({unit})": stat for (x, unit), stat in zip(gpu_stat_metrics, stats)}
return gpu_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()