def test_pick_multiple_gpus(nb, expected_gpu_idxs, expected_error):
if expected_error:
with pytest.raises(
expected_error,
match=re.escape(
"auto_select_gpus=True, gpus=0 is not a valid configuration."
" Please select a valid number of GPU resources when using auto_select_gpus."
),
):
pick_multiple_gpus(nb)
else:
assert expected_gpu_idxs == pick_multiple_gpus(nb)
def _parse_devices(
gpus: Optional[Union[List[int], str, int]],
auto_select_gpus: bool,
tpu_cores: Optional[Union[List[int], str, int]],
) -> Tuple[Optional[List[int]], Optional[Union[List[int], int]]]:
if auto_select_gpus and isinstance(gpus, int):
gpus = pick_multiple_gpus(gpus)
# TODO (@seannaren, @kaushikb11): Include IPU parsing logic here
gpu_ids = parse_gpu_ids(gpus)
tpu_cores = parse_tpu_cores(tpu_cores)
return gpu_ids, tpu_cores
def __init__(
self,
num_processes,
tpu_cores,
ipus,
distributed_backend,
auto_select_gpus,
gpus,
num_nodes,
sync_batchnorm,
benchmark,
replace_sampler_ddp,
deterministic,
precision,
amp_type,
amp_level,
plugins,
):
# initialization
self._device_type = DeviceType.CPU
self._distrib_type = None
self.num_processes = num_processes
self.tpu_cores = device_parser.parse_tpu_cores(tpu_cores)
self.ipus = ipus
self.distributed_backend = distributed_backend
self.auto_select_gpus = auto_select_gpus
self.gpus = gpus
self.num_nodes = num_nodes
self.sync_batchnorm = sync_batchnorm
self.benchmark = benchmark
self.replace_sampler_ddp = replace_sampler_ddp
self.deterministic = deterministic
self.precision = precision
self.amp_type = amp_type.lower() if isinstance(amp_type, str) else None
self.amp_level = amp_level
self.is_slurm_managing_tasks = False
self._precision_plugin: Optional[PrecisionPlugin] = None
self._training_type_plugin: Optional[TrainingTypePlugin] = None
self._cluster_environment: Optional[ClusterEnvironment] = None
plugins = plugins if plugins is not None else []
if isinstance(plugins, str):
plugins = [plugins]
if not isinstance(plugins, Sequence):
plugins = [plugins]
self.plugins = plugins
# for gpus allow int, string and gpu list
if auto_select_gpus and isinstance(gpus, int):
self.gpus = pick_multiple_gpus(gpus)
self.parallel_device_ids = device_parser.parse_gpu_ids(self.gpus)
self.set_distributed_mode()
self.configure_slurm_ddp()
self.handle_given_plugins()
self._training_type_plugin_resolved = False
self.accelerator = self.select_accelerator()
# override dist backend when using tpus
if self.on_tpu:
self.distributed_backend = "tpu"
# init flags for SLURM+DDP to work
self.world_size = 1
self.interactive_ddp_procs = []
self.global_rank = 0
# benchmarking
# TODO: should this be moved to GPU accelerator?
torch.backends.cudnn.benchmark = self.benchmark
# determinism for cudnn
# TODO: should this be moved to GPU accelerator?
torch.backends.cudnn.deterministic = deterministic
if deterministic:
# fixing non-deterministic part of horovod
# https://github.com/PyTorchLightning/pytorch-lightning/pull/1572/files#r420279383
os.environ["HOROVOD_FUSION_THRESHOLD"] = str(0)
self.replace_sampler_ddp = replace_sampler_ddp
def __init__(
self,
num_processes,
tpu_cores,
distributed_backend,
auto_select_gpus,
gpus,
num_nodes,
sync_batchnorm,
benchmark,
replace_sampler_ddp,
deterministic,
precision,
amp_type,
amp_level,
cluster_environment,
):
# initialization
self._device_type = DeviceType.CPU
self._distrib_type = None
self.num_processes = num_processes
self.tpu_cores = device_parser.parse_tpu_cores(tpu_cores)
self.distributed_backend = distributed_backend
self.auto_select_gpus = auto_select_gpus
self.gpus = gpus
self.num_nodes = num_nodes
self.sync_batchnorm = sync_batchnorm
self.benchmark = benchmark
self.replace_sampler_ddp = replace_sampler_ddp
self.deterministic = deterministic
self.precision = precision
self.amp_type = amp_type.lower() if isinstance(amp_type, str) else None
self.amp_level = amp_level
self.cluster_environment = cluster_environment
self.is_slurm_managing_tasks = False
# init the default rank if exists
# we need to call this here or NVIDIA flags and other messaging in init will show on all ranks
# this way we only show it on rank 0
if "LOCAL_RANK" in os.environ:
rank_zero_only.rank = int(os.environ["LOCAL_RANK"])
# for gpus allow int, string and gpu list
if auto_select_gpus and isinstance(gpus, int):
self.gpus = pick_multiple_gpus(gpus)
self.parallel_device_ids = device_parser.parse_gpu_ids(self.gpus)
self.root_gpu = device_parser.determine_root_gpu_device(
self.parallel_device_ids)
self.set_distributed_mode()
self.configure_slurm_ddp()
self.accelerator = self.select_accelerator()
# override dist backend when using tpus
if self.on_tpu:
self.distributed_backend = "tpu"
self.use_tpu = True
# init flags for SLURM+DDP to work
self.world_size = 1
self.interactive_ddp_procs = []
self.global_rank = 0
# NVIDIA setup
# self.set_nvidia_flags(self.trainer.is_slurm_managing_tasks, self.trainer.data_parallel_device_ids)
# benchmarking
# TODO: should this be moved to GPU accelerator?
torch.backends.cudnn.benchmark = self.benchmark
# determinism for cudnn
# TODO: should this be moved to GPU accelerator?
torch.backends.cudnn.deterministic = deterministic
if deterministic:
# fixing non-deterministic part of horovod
# https://github.com/PyTorchLightning/pytorch-lightning/pull/1572/files#r420279383
os.environ["HOROVOD_FUSION_THRESHOLD"] = str(0)
# TODO: move this to TPU accelerator/plugin
self.on_colab_kaggle = os.getenv("COLAB_GPU") or os.getenv(
"KAGGLE_URL_BASE")
self.replace_sampler_ddp = replace_sampler_ddp
def pick_multiple_gpus(self, num_gpus: int):
return pick_multiple_gpus(num_gpus)
def test_pick_multiple_gpus_more_than_available(*_):
with pytest.raises(
MisconfigurationException,
match="You requested 3 GPUs but your machine only has 1 GPUs"):
pick_multiple_gpus(3)
def _set_devices_flag_if_auto_select_gpus_passed(self) -> None:
if self._auto_select_gpus and isinstance(self._gpus, int) and isinstance(self.accelerator, GPUAccelerator):
self._devices_flag = pick_multiple_gpus(self._gpus)
log.info(f"Auto select gpus: {self._devices_flag}")
