class AcceleratorConnector(object):
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 handle_given_plugins(self) -> None:
training_type = None
precision = None
cluster_environment = None
for plug in self.plugins:
if isinstance(plug, str) and plug in TrainingTypePluginsRegistry:
if training_type is None:
training_type = TrainingTypePluginsRegistry.get(plug)
else:
raise MisconfigurationException(
'You can only specify one precision and one training type plugin.'
' Found more than 1 training type plugin:'
f' {TrainingTypePluginsRegistry[plug]["plugin"]} registered to {plug}'
)
if isinstance(plug, str):
# Reset the distributed type as the user has overridden training type
# via the plugins argument
self._distrib_type = None
self.set_distributed_mode(plug)
elif isinstance(plug, TrainingTypePlugin):
if training_type is None:
training_type = plug
else:
raise MisconfigurationException(
'You can only specify one precision and one training type plugin.'
f' Found more than 1 training type plugin: {type(plug).__name__}'
)
elif isinstance(plug, PrecisionPlugin):
if precision is None:
precision = plug
else:
raise MisconfigurationException(
'You can only specify one precision and one training type plugin.'
f' Found more than 1 precision plugin: {type(plug).__name__}'
)
elif isinstance(plug, ClusterEnvironment):
if cluster_environment is None:
cluster_environment = plug
else:
raise MisconfigurationException(
'You can only specify one cluster environment. Found more than 1 cluster environment plugin'
)
else:
raise MisconfigurationException(
f'Found invalid type for plugin {plug}. Expected a precision or training type plugin.'
)
self._training_type_plugin = training_type
self._precision_plugin = precision
self._cluster_environment = cluster_environment or self.select_cluster_environment()
@property
def precision_plugin(self) -> PrecisionPlugin:
if self._precision_plugin is None:
self._precision_plugin = self.select_precision_plugin()
return self._precision_plugin
@property
def training_type_plugin(self) -> TrainingTypePlugin:
if self._training_type_plugin_resolved:
# avoid calling `resolve_training_type_plugin` multiple times
return self._training_type_plugin
if self._training_type_plugin is None:
self._training_type_plugin = self.select_training_type_plugin()
self._training_type_plugin = self.resolve_training_type_plugin(self._training_type_plugin)
self._training_type_plugin_resolved = True
return self._training_type_plugin
@property
def cluster_environment(self) -> ClusterEnvironment:
return self._cluster_environment
@property
def on_cpu(self) -> bool:
return self._device_type == DeviceType.CPU
@property
def on_tpu(self) -> bool:
return self.tpu_cores is not None
@property
def on_ipu(self) -> bool:
return self.ipus is not None
@property
def tpu_id(self) -> Optional[int]:
if self.on_tpu and isinstance(self.tpu_cores, list):
return self.tpu_cores[0]
return None
@property
def on_gpu(self) -> bool:
gpus = self.parallel_device_ids
return gpus is not None and len(gpus) > 0 and torch.cuda.is_available()
@property
def use_dp(self) -> bool:
return self._distrib_type == DistributedType.DP
@property
def use_ddp(self) -> bool:
return self._distrib_type in (
DistributedType.DDP,
DistributedType.DDP_SPAWN,
DistributedType.DDP_SHARDED,
DistributedType.DDP_SHARDED_SPAWN,
DistributedType.DDP_FULLY_SHARDED,
DistributedType.DEEPSPEED,
DistributedType.TPU_SPAWN,
)
@property
def use_ddp2(self) -> bool:
return self._distrib_type == DistributedType.DDP2
@property
def use_horovod(self) -> bool:
return self._distrib_type == DistributedType.HOROVOD
@property
def use_deepspeed(self) -> bool:
return self._distrib_type == DistributedType.DEEPSPEED
@property
def _is_sharded_training_type(self) -> bool:
return isinstance(self.training_type_plugin, (DDPShardedPlugin, DDPSpawnShardedPlugin))
@property
def _is_fully_sharded_training_type(self) -> bool:
return isinstance(self.training_type_plugin, DDPFullyShardedPlugin)
@property
def is_distributed(self) -> bool:
# Used for custom plugins.
# Custom plugins should implement is_distributed property.
if hasattr(self.training_type_plugin, 'is_distributed') and not self.on_tpu:
return self.training_type_plugin.is_distributed
is_distributed = self.use_ddp or self.use_ddp2 or self.use_horovod
if self.on_tpu:
is_distributed |= self.training_type_plugin.is_distributed
return is_distributed
@property
def num_gpus(self) -> int:
gpus = self.parallel_device_ids
if gpus is None:
return 0
return len(gpus)
@property
def parallel_devices(self) -> List[Union[torch.device, int]]:
if self.on_gpu:
devices = [torch.device("cuda", i) for i in self.parallel_device_ids]
elif self.on_tpu:
# explicitly don't make a tpu device here!
# https://github.com/PyTorchLightning/pytorch-lightning/issues/3169
if isinstance(self.tpu_cores, int):
devices = list(range(self.tpu_cores))
elif self.on_ipu:
if isinstance(self.ipus, int):
devices = list(range(self.ipus))
else:
devices = [torch.device("cpu")] * self.num_processes
return devices
@property
def root_gpu(self) -> Optional[int]:
return self.accelerator.root_device.index if not isinstance(
self.accelerator, (IPUAccelerator, TPUAccelerator)
) else None
@property
def is_training_type_in_plugins(self) -> bool:
return any(isinstance(plug, str) and plug in TrainingTypePluginsRegistry for plug in self.plugins)
@property
def is_using_torchelastic(self) -> bool:
"""
.. deprecated:: v1.3
Will be removed in v1.5.0.
Returns:
``True`` if the current process was launched using the torchelastic command.
"""
rank_zero_deprecation(
"The property `AcceleratorConnector.is_using_torchelastic` was deprecated in v1.3"
" and will be removed in 1.5. Use `TorchElasticEnvironment.is_using_torchelastic()` instead.",
)
return TorchElasticEnvironment.is_using_torchelastic()
def select_precision_plugin(self) -> PrecisionPlugin:
# set precision type
self.amp_type = AMPType.from_str(self.amp_type)
if self.on_ipu:
return IPUPrecisionPlugin(self.precision)
if self._distrib_type == DistributedType.DEEPSPEED or isinstance(self._training_type_plugin, DeepSpeedPlugin):
return DeepSpeedPrecisionPlugin(self.precision)
if self.precision == 32:
return PrecisionPlugin()
elif self.precision == 64:
return DoublePrecisionPlugin()
elif self.precision == 16:
if self.on_tpu:
return TPUHalfPrecisionPlugin()
if self.amp_type == AMPType.NATIVE:
if self.on_cpu:
raise MisconfigurationException(
"You have asked for native AMP on CPU, but AMP is only available on GPU."
)
elif not _NATIVE_AMP_AVAILABLE:
msg = "You have asked for native AMP but your PyTorch version does not support it." \
" Consider upgrading with `pip install torch>=1.6`."
if _APEX_AVAILABLE:
self.amp_type = AMPType.APEX
msg += " We will attempt to use NVIDIA Apex for this session."
rank_zero_warn(msg)
else:
raise MisconfigurationException(msg)
else:
log.info("Using native 16bit precision.")
if self._is_sharded_training_type:
return ShardedNativeMixedPrecisionPlugin()
if self._is_fully_sharded_training_type:
return FullyShardedNativeMixedPrecisionPlugin()
return NativeMixedPrecisionPlugin()
if self.amp_type == AMPType.APEX:
if not _APEX_AVAILABLE:
raise MisconfigurationException(
"You have asked for Apex AMP but you have not installed it yet."
" Install apex first using this guide: https://github.com/NVIDIA/apex#linux"
)
if self._is_sharded_training_type or self._is_fully_sharded_training_type:
raise MisconfigurationException(
"Sharded Plugin is not supported with Apex AMP,"
" please using native AMP for 16-bit precision."
)
log.info("Using APEX 16bit precision.")
return ApexMixedPrecisionPlugin(self.amp_level)
raise NotImplementedError("We only support precisions 64, 32 and 16!")
def select_training_type_plugin(self) -> TrainingTypePlugin:
if isinstance(
self.distributed_backend, Accelerator
) and self.distributed_backend.training_type_plugin is not None:
plugin = self.distributed_backend.training_type_plugin
elif self.use_ddp2:
plugin = DDP2Plugin(
parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment,
)
elif self.use_ddp and self.use_deepspeed:
plugin = DeepSpeedPlugin(
cluster_environment=self.select_cluster_environment(), parallel_devices=self.parallel_devices
)
elif self.use_ddp:
use_slurm_ddp = self.use_ddp and self.is_slurm_managing_tasks
use_torchelastic_ddp = self.use_ddp and TorchElasticEnvironment.is_using_torchelastic()
use_kubeflow_ddp = self.use_ddp and KubeflowEnvironment.is_using_kubeflow()
use_ddp_spawn = self._distrib_type == DistributedType.DDP_SPAWN
use_ddp_cpu_spawn = self.use_ddp and self.on_cpu
use_tpu_spawn = self.on_tpu and self._distrib_type == DistributedType.TPU_SPAWN
use_ddp_cpu_torch_elastic = use_ddp_cpu_spawn and TorchElasticEnvironment.is_using_torchelastic()
use_ddp_cpu_kubeflow = use_ddp_cpu_spawn and KubeflowEnvironment.is_using_kubeflow()
use_ddp_cpu_slurm = use_ddp_cpu_spawn and self.is_slurm_managing_tasks
use_ddp_sharded = self._distrib_type == DistributedType.DDP_SHARDED
use_ddp_sharded_spawn = self._distrib_type == DistributedType.DDP_SHARDED_SPAWN
use_ddp_fully_sharded = self._distrib_type == DistributedType.DDP_FULLY_SHARDED
# TODO: decouple from TE
# ddp script mode uses the same flags as TE
if os.environ.get("PL_IN_DDP_SUBPROCESS", False):
use_torchelastic_ddp = False
if use_tpu_spawn:
ddp_plugin_cls = TPUSpawnPlugin
elif use_ddp_sharded:
ddp_plugin_cls = DDPShardedPlugin
elif use_ddp_sharded_spawn:
ddp_plugin_cls = DDPSpawnShardedPlugin
elif (
use_ddp_cpu_slurm or use_slurm_ddp or use_ddp_cpu_torch_elastic or use_torchelastic_ddp
or use_kubeflow_ddp or use_ddp_cpu_kubeflow
):
ddp_plugin_cls = DDPPlugin
elif use_ddp_spawn or use_ddp_cpu_spawn:
ddp_plugin_cls = DDPSpawnPlugin
elif use_ddp_fully_sharded:
ddp_plugin_cls = DDPFullyShardedPlugin
else:
ddp_plugin_cls = DDPPlugin
plugin = ddp_plugin_cls(
parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment,
)
elif self.use_dp:
plugin = DataParallelPlugin(parallel_devices=self.parallel_devices)
elif self.use_horovod:
plugin = HorovodPlugin(parallel_devices=self.parallel_devices)
elif self.on_tpu and isinstance(self.tpu_cores, list):
plugin = SingleTPUPlugin(self.tpu_id)
elif self.on_ipu:
plugin = IPUPlugin(parallel_devices=self.parallel_devices)
else:
single_gpu_ordinal = device_parser.determine_root_gpu_device(self.parallel_device_ids)
plugin = SingleDevicePlugin(device=torch.device(f"cuda:{single_gpu_ordinal}" if self.on_gpu else "cpu"))
return plugin
def resolve_training_type_plugin(self, training_type: TrainingTypePlugin) -> TrainingTypePlugin:
# necessary for when the user has passed in a plugin
if hasattr(training_type, 'parallel_devices') and getattr(training_type, 'parallel_devices') is None:
training_type.parallel_devices = self.parallel_devices
if hasattr(training_type, 'num_processes'):
training_type.num_processes = len(self.parallel_devices)
if hasattr(training_type, 'cluster_environment') and getattr(training_type, 'cluster_environment') is None:
training_type.cluster_environment = self.select_cluster_environment()
if hasattr(training_type, 'num_nodes'):
# set num_nodes for training_type from trainer setting
training_type.num_nodes = self.num_nodes
if hasattr(training_type, 'sync_batchnorm'):
# set sync_batchnorm for training_type from trainer setting
training_type.sync_batchnorm = self.sync_batchnorm
return training_type
def select_accelerator(self) -> Accelerator:
if isinstance(self.distributed_backend, Accelerator):
# custom accelerator from user
if self._precision_plugin is not None or self._training_type_plugin is not None:
# plugins also specified by user
rank_zero_warn(
'Specified `Precision` and `TrainingType` plugins will be ignored,'
' since an `Accelerator` instance was provided.'
)
return self.distributed_backend
if self.on_gpu:
acc_cls = GPUAccelerator
elif self.on_tpu:
acc_cls = TPUAccelerator
elif self.on_ipu:
acc_cls = IPUAccelerator
else:
acc_cls = CPUAccelerator
# as precision_plugin is dependent on training_type_plugin, make sure
# that we first select training_type_plugin, then precision_plugin
return acc_cls(
training_type_plugin=self.training_type_plugin,
precision_plugin=self.precision_plugin,
)
def select_cluster_environment(self) -> ClusterEnvironment:
if self._cluster_environment is not None:
return self._cluster_environment
if self.is_slurm_managing_tasks:
env = SLURMEnvironment()
elif TorchElasticEnvironment.is_using_torchelastic():
env = TorchElasticEnvironment()
elif KubeflowEnvironment.is_using_kubeflow():
env = KubeflowEnvironment()
else:
env = LightningEnvironment()
return env
def set_distributed_mode(self, distributed_backend: Optional[str] = None):
if distributed_backend is None and self.is_training_type_in_plugins:
return
if distributed_backend is not None and distributed_backend in TrainingTypePluginsRegistry:
self.distributed_backend = TrainingTypePluginsRegistry[distributed_backend]["distributed_backend"]
elif distributed_backend is not None:
self.distributed_backend = distributed_backend
if isinstance(self.distributed_backend, Accelerator):
return
if self.distributed_backend is None:
if self.has_horovodrun():
self._set_horovod_backend()
elif self.num_gpus == 0 and (self.num_nodes > 1 or self.num_processes > 1):
self._distrib_type = DistributedType.DDP
elif self.num_gpus > 1:
rank_zero_warn(
'You requested multiple GPUs but did not specify a backend, e.g.'
' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
)
self.distributed_backend = "ddp_spawn"
# special case with DDP on CPUs
if self.distributed_backend == "ddp_cpu":
self._distrib_type = DistributedType.DDP_SPAWN
if self.num_gpus > 0:
rank_zero_warn(
'You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs.'
)
self.parallel_device_ids = None
if self.num_processes is None:
# define the max CPU available
self.num_processes = os.cpu_count()
# special case with TPUs
elif self.distributed_backend == 'tpu' or self.tpu_cores is not None:
self._device_type = DeviceType.TPU
if isinstance(self.tpu_cores, int):
self._distrib_type = DistributedType.TPU_SPAWN
elif self.distributed_backend == 'ipu':
self._device_type = DeviceType.IPU
elif self.distributed_backend and self._distrib_type is None:
self._distrib_type = DistributedType(self.distributed_backend)
# unless you request explicitly for CPU and some GPU are available use them
_on_cpu = self.distributed_backend and 'cpu' in self.distributed_backend
if self.num_gpus > 0 and not _on_cpu:
self._device_type = DeviceType.GPU
_gpu_distrib_types = (DistributedType.DP, DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2)
# DP and DDP2 cannot run without GPU
if self.num_gpus == 0 and self._distrib_type in _gpu_distrib_types and not _on_cpu:
rank_zero_warn(
'You requested distributed training on GPUs, but none is available, so we set backend to `ddp_cpu`.'
)
# todo: in some cases it yield in comparison None and int
if (self.num_nodes and self.num_nodes > 1) or (self.num_processes and self.num_processes > 1):
self._distrib_type = DistributedType.DDP
else:
rank_zero_warn('You are running on single node with no parallelization, so distributed has no effect.')
self._distrib_type = None
# finished configuring self._distrib_type, check ipython environment
self.check_interactive_compatibility()
# for DDP overwrite nb processes by requested GPUs
if (
self._device_type == DeviceType.GPU
and self._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN)
):
self.num_processes = self.num_gpus
if (self._device_type == DeviceType.GPU and self._distrib_type == DistributedType.DDP2):
self.num_processes = self.num_nodes
# Horovod is an extra case...
if self.distributed_backend == "horovod":
self._set_horovod_backend()
using_valid_distributed = self.use_ddp or self.use_ddp2
if self.num_nodes > 1 and not using_valid_distributed:
# throw error to force user to choose a supported distributed type such as ddp or ddp2
raise MisconfigurationException(
'Your chosen distributed type does not support num_nodes > 1. '
'Please set accelerator=ddp or accelerator=ddp2.'
)
rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self._device_type == DeviceType.GPU}')
num_tpu_cores = self.tpu_cores if self.tpu_cores is not None else 0
rank_zero_info(f'TPU available: {_TPU_AVAILABLE}, using: {num_tpu_cores} TPU cores')
num_ipus = self.ipus if self.ipus is not None else 0
rank_zero_info(f'IPU available: {_IPU_AVAILABLE}, using: {num_ipus} IPUs')
if torch.cuda.is_available() and self._device_type != DeviceType.GPU:
rank_zero_warn(
"GPU available but not used. Set the gpus flag in your trainer"
" `Trainer(gpus=1)` or script `--gpus=1`."
)
def _set_horovod_backend(self):
self.check_horovod()
self._distrib_type = DistributedType.HOROVOD
# Initialize Horovod to get rank / size info
hvd.init()
if self.on_gpu:
# Horovod assigns one local GPU per process
self.parallel_device_ids = list(range(hvd.local_size()))
else:
self.num_processes = hvd.local_size()
def check_interactive_compatibility(self):
"""
Raises a `MisconfigurationException` if the accelerator and/or plugin
is not compatible with an interactive environment
"""
from pytorch_lightning.utilities import _IS_INTERACTIVE
if _IS_INTERACTIVE and self._distrib_type is not None and not self._distrib_type.is_interactive_compatible():
raise MisconfigurationException(
f"Selected distributed backend {self._distrib_type} is not compatible with an interactive"
" environment. Run your code as a script, or choose one of the compatible backends:"
f" {', '.join(DistributedType.interactive_compatible_types())}"
)
def check_horovod(self):
"""Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
if not _HOROVOD_AVAILABLE:
raise MisconfigurationException(
'Requested `distributed_backend="horovod"`, but Horovod is not installed.'
"Install with \n $HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]"
)
if self.num_gpus > 1 or self.num_nodes > 1:
raise MisconfigurationException(
"Horovod does not support setting num_nodes / num_gpus explicitly. Use "
"horovodrun / mpirun to configure the number of processes."
)
@staticmethod
def has_horovodrun() -> bool:
"""Returns True if running with `horovodrun` using Gloo or OpenMPI."""
return "OMPI_COMM_WORLD_RANK" in os.environ or "HOROVOD_RANK" in os.environ
def configure_slurm_ddp(self):
# extract SLURM flag vars
# whenever we have the correct number of tasks, we let slurm manage processes
# otherwise we launch the required number of processes
if self.use_ddp or self.use_ddp2:
num_requested_gpus = self.num_gpus * self.num_nodes
num_slurm_tasks = 0
try:
num_slurm_tasks = int(os.environ["SLURM_NTASKS"])
self.is_slurm_managing_tasks = num_slurm_tasks == num_requested_gpus
# enable slurm cpu
if num_requested_gpus == 0:
self.is_slurm_managing_tasks = num_slurm_tasks == self.num_processes
# in interactive mode we don't manage tasks
job_name = os.environ["SLURM_JOB_NAME"]
if job_name == "bash":
self.is_slurm_managing_tasks = False
except Exception:
# likely not on slurm, so set the slurm managed flag to false
self.is_slurm_managing_tasks = False
# used for tests only, set this flag to simulate slurm managing a task
try:
should_fake = int(os.environ["FAKE_SLURM_MANAGING_TASKS"])
if should_fake:
self.is_slurm_managing_tasks = True
except Exception:
pass
# notify user the that slurm is managing tasks
if self.is_slurm_managing_tasks:
rank_zero_info("Multi-processing is handled by Slurm.")
def set_distributed_mode(self, distributed_backend: Optional[str] = None):
if distributed_backend is None and self.is_training_type_in_plugins:
return
if distributed_backend is not None and distributed_backend in TrainingTypePluginsRegistry:
self.distributed_backend = TrainingTypePluginsRegistry[distributed_backend]["distributed_backend"]
elif distributed_backend is not None:
self.distributed_backend = distributed_backend
if isinstance(self.distributed_backend, Accelerator):
return
if self.distributed_backend is None:
if self.has_horovodrun():
self._set_horovod_backend()
elif self.num_gpus == 0 and (self.num_nodes > 1 or self.num_processes > 1):
self._distrib_type = DistributedType.DDP
elif self.num_gpus > 1:
rank_zero_warn(
'You requested multiple GPUs but did not specify a backend, e.g.'
' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
)
self.distributed_backend = "ddp_spawn"
# special case with DDP on CPUs
if self.distributed_backend == "ddp_cpu":
self._distrib_type = DistributedType.DDP_SPAWN
if self.num_gpus > 0:
rank_zero_warn(
'You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs.'
)
self.parallel_device_ids = None
if self.num_processes is None:
# define the max CPU available
self.num_processes = os.cpu_count()
# special case with TPUs
elif self.distributed_backend == 'tpu' or self.tpu_cores is not None:
self._device_type = DeviceType.TPU
if isinstance(self.tpu_cores, int):
self._distrib_type = DistributedType.TPU_SPAWN
elif self.distributed_backend == 'ipu':
self._device_type = DeviceType.IPU
elif self.distributed_backend and self._distrib_type is None:
self._distrib_type = DistributedType(self.distributed_backend)
# unless you request explicitly for CPU and some GPU are available use them
_on_cpu = self.distributed_backend and 'cpu' in self.distributed_backend
if self.num_gpus > 0 and not _on_cpu:
self._device_type = DeviceType.GPU
_gpu_distrib_types = (DistributedType.DP, DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2)
# DP and DDP2 cannot run without GPU
if self.num_gpus == 0 and self._distrib_type in _gpu_distrib_types and not _on_cpu:
rank_zero_warn(
'You requested distributed training on GPUs, but none is available, so we set backend to `ddp_cpu`.'
)
# todo: in some cases it yield in comparison None and int
if (self.num_nodes and self.num_nodes > 1) or (self.num_processes and self.num_processes > 1):
self._distrib_type = DistributedType.DDP
else:
rank_zero_warn('You are running on single node with no parallelization, so distributed has no effect.')
self._distrib_type = None
# finished configuring self._distrib_type, check ipython environment
self.check_interactive_compatibility()
# for DDP overwrite nb processes by requested GPUs
if (
self._device_type == DeviceType.GPU
and self._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN)
):
self.num_processes = self.num_gpus
if (self._device_type == DeviceType.GPU and self._distrib_type == DistributedType.DDP2):
self.num_processes = self.num_nodes
# Horovod is an extra case...
if self.distributed_backend == "horovod":
self._set_horovod_backend()
using_valid_distributed = self.use_ddp or self.use_ddp2
if self.num_nodes > 1 and not using_valid_distributed:
# throw error to force user to choose a supported distributed type such as ddp or ddp2
raise MisconfigurationException(
'Your chosen distributed type does not support num_nodes > 1. '
'Please set accelerator=ddp or accelerator=ddp2.'
)
rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self._device_type == DeviceType.GPU}')
num_tpu_cores = self.tpu_cores if self.tpu_cores is not None else 0
rank_zero_info(f'TPU available: {_TPU_AVAILABLE}, using: {num_tpu_cores} TPU cores')
num_ipus = self.ipus if self.ipus is not None else 0
rank_zero_info(f'IPU available: {_IPU_AVAILABLE}, using: {num_ipus} IPUs')
if torch.cuda.is_available() and self._device_type != DeviceType.GPU:
rank_zero_warn(
"GPU available but not used. Set the gpus flag in your trainer"
" `Trainer(gpus=1)` or script `--gpus=1`."
)
def set_distributed_mode(self, distributed_backend: Optional[str] = None):
if distributed_backend is None and self.is_training_type_in_plugins:
return
if distributed_backend is not None and distributed_backend in TrainingTypePluginsRegistry:
self.distributed_backend = TrainingTypePluginsRegistry[
distributed_backend]["distributed_backend"]
elif distributed_backend is not None:
self.distributed_backend = distributed_backend
if isinstance(self.distributed_backend, Accelerator):
return
is_cpu_accelerator_type = self._accelerator_type and self._accelerator_type == DeviceType.CPU
_use_cpu = is_cpu_accelerator_type or self.distributed_backend and "cpu" in self.distributed_backend
if self.distributed_backend is None:
if self.has_horovodrun():
self._set_horovod_backend()
elif self.num_gpus == 0 and self.num_nodes > 1:
self._distrib_type = DistributedType.DDP
elif self.num_gpus == 0 and self.num_processes > 1:
self.distributed_backend = DistributedType.DDP_SPAWN
elif self.num_gpus > 1 and not _use_cpu:
rank_zero_warn(
"You requested multiple GPUs but did not specify a backend, e.g."
' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
)
self.distributed_backend = DistributedType.DDP_SPAWN
# special case with DDP on CPUs
if self.distributed_backend == DistributedType.DDP_CPU:
if _TPU_AVAILABLE:
raise MisconfigurationException(
"`accelerator='ddp_cpu'` is not supported on TPU machines. "
"Learn more: https://github.com/PyTorchLightning/pytorch-lightning/issues/7810"
)
self._distrib_type = DistributedType.DDP_SPAWN
if self.num_gpus > 0:
rank_zero_warn(
"You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs."
)
self.parallel_device_ids = None
if self.num_processes is None:
# define the max CPU available
self.num_processes = os.cpu_count()
# special case with TPUs
elif self.has_tpu and not _use_cpu:
self._device_type = DeviceType.TPU
if isinstance(self.tpu_cores, int):
self._distrib_type = DistributedType.TPU_SPAWN
elif self.has_ipu and not _use_cpu:
self._device_type = DeviceType.IPU
elif self.distributed_backend and self._distrib_type is None:
self._distrib_type = DistributedType(self.distributed_backend)
if self.num_gpus > 0 and not _use_cpu:
self._device_type = DeviceType.GPU
_gpu_distrib_types = (DistributedType.DP, DistributedType.DDP,
DistributedType.DDP_SPAWN, DistributedType.DDP2)
# DP and DDP2 cannot run without GPU
if self.num_gpus == 0 and self._distrib_type in _gpu_distrib_types and not _use_cpu:
if (self.num_nodes
and self.num_nodes > 1) or (self.num_processes
and self.num_processes > 1):
if self._distrib_type in (DistributedType.DP,
DistributedType.DDP2):
rank_zero_warn(
f"{self._distrib_type} is not supported on CPUs, hence setting the distributed type to `ddp`."
)
self._distrib_type = DistributedType.DDP
else:
rank_zero_warn(
"You are running on single node with no parallelization, so distributed has no effect."
)
self._distrib_type = None
# finished configuring self._distrib_type, check ipython environment
self.check_interactive_compatibility()
# for DDP overwrite nb processes by requested GPUs
if self._device_type == DeviceType.GPU and self._distrib_type in (
DistributedType.DDP,
DistributedType.DDP_SPAWN,
):
self.num_processes = self.num_gpus
if self._device_type == DeviceType.GPU and self._distrib_type == DistributedType.DDP2:
self.num_processes = self.num_nodes
# Horovod is an extra case...
if self.distributed_backend == DistributedType.HOROVOD:
self._set_horovod_backend()
using_valid_distributed = self.use_ddp or self.use_ddp2
if self.num_nodes > 1 and not using_valid_distributed:
# throw error to force user to choose a supported distributed type such as ddp or ddp2
raise MisconfigurationException(
"Your chosen distributed type does not support num_nodes > 1. "
"Please set accelerator=ddp or accelerator=ddp2.")
class AcceleratorConnector:
def __init__(
self,
num_processes,
devices,
tpu_cores,
ipus,
distributed_backend,
accelerator,
gpus,
gpu_ids,
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._accelerator_type = None
if distributed_backend is not None:
rank_zero_deprecation(
f"`Trainer(distributed_backend={distributed_backend})` has been deprecated and will be removed in v1.5."
f" Use `Trainer(accelerator={distributed_backend})` instead.")
distributed_backend = distributed_backend or accelerator
self.num_processes = num_processes
self.devices = devices
# `gpus` is the input passed to the Trainer, whereas `gpu_ids` is a list of parsed gpu ids.
self.gpus = gpus
self.parallel_device_ids = gpu_ids
self.tpu_cores = tpu_cores
self.ipus = ipus
self.distributed_backend = distributed_backend
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
self._checkpoint_io: Optional[CheckpointIO] = 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
self._validate_accelerator_and_devices()
self._warn_if_devices_flag_ignored()
self.select_accelerator_type()
self.set_distributed_mode()
self.configure_slurm_ddp()
self.handle_given_plugins()
self.update_device_type_if_ipu_plugin()
self._validate_accelerator_type()
self._set_devices_if_none()
self._training_type_plugin_resolved = False
self.accelerator = self.select_accelerator()
# override dist backend when using tpus
if self.use_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 select_accelerator_type(self) -> None:
if self.distributed_backend == "auto":
if self.has_tpu:
self._accelerator_type = DeviceType.TPU
elif self.has_ipu:
self._accelerator_type = DeviceType.IPU
elif self.has_gpu:
self._accelerator_type = DeviceType.GPU
else:
self._set_devices_to_cpu_num_processes()
self._accelerator_type = DeviceType.CPU
elif self.distributed_backend == DeviceType.TPU:
if not self.has_tpu:
msg = "TPUs are not available" if not _TPU_AVAILABLE else "you didn't pass `tpu_cores` to `Trainer`"
raise MisconfigurationException(
f"You passed `accelerator='tpu'`, but {msg}.")
self._accelerator_type = DeviceType.TPU
elif self.distributed_backend == DeviceType.IPU:
if not self.has_ipu:
msg = "IPUs are not available" if not _IPU_AVAILABLE else "you didn't pass `ipus` to `Trainer`"
raise MisconfigurationException(
f"You passed `accelerator='ipu'`, but {msg}.")
self._accelerator_type = DeviceType.IPU
elif self.distributed_backend == DeviceType.GPU:
if not self.has_gpu:
msg = "you didn't pass `gpus` to `Trainer`" if torch.cuda.is_available(
) else "GPUs are not available"
raise MisconfigurationException(
f"You passed `accelerator='gpu'`, but {msg}.")
self._accelerator_type = DeviceType.GPU
elif self.distributed_backend == DeviceType.CPU:
self._set_devices_to_cpu_num_processes()
self._accelerator_type = DeviceType.CPU
if self.distributed_backend in ["auto"] + list(DeviceType):
self.distributed_backend = None
def _validate_accelerator_and_devices(self) -> None:
if self.distributed_backend not in ["auto"] + list(
DeviceType) and self.devices is not None:
raise MisconfigurationException(
f"You passed `devices={self.devices}` but haven't specified"
" `accelerator=('auto'|'tpu'|'gpu'|'ipu'|'cpu')` for the devices mapping,"
f" got `accelerator={self.distributed_backend}`.")
def _validate_accelerator_type(self) -> None:
if self._accelerator_type and self._accelerator_type != self._device_type:
raise MisconfigurationException(
f"Mismatch between the requested accelerator type ({self._accelerator_type})"
f" and assigned device type ({self._device_type}).")
self._accelerator_type = self._device_type
def _warn_if_devices_flag_ignored(self) -> None:
if self.devices is None:
return
devices_warning = f"The flag `devices={self.devices}` will be ignored, as you have set"
if self.distributed_backend == "auto":
if self.tpu_cores is not None:
rank_zero_warn(
f"{devices_warning} `tpu_cores={self.tpu_cores}`")
elif self.ipus is not None:
rank_zero_warn(f"{devices_warning} `ipus={self.ipus}`")
elif self.gpus is not None:
rank_zero_warn(f"{devices_warning} `gpus={self.gpus}`")
elif self.num_processes != 1:
rank_zero_warn(
f"{devices_warning} `num_processes={self.num_processes}`")
elif self.distributed_backend == DeviceType.TPU:
if self.tpu_cores is not None:
rank_zero_warn(
f"{devices_warning} `tpu_cores={self.tpu_cores}`")
elif self.distributed_backend == DeviceType.IPU:
if self.ipus is not None:
rank_zero_warn(f"{devices_warning} `ipus={self.ipus}`")
elif self.distributed_backend == DeviceType.GPU:
if self.gpus is not None:
rank_zero_warn(f"{devices_warning} `gpus={self.gpus}`")
elif self.distributed_backend == DeviceType.CPU:
if self.num_processes != 1:
rank_zero_warn(
f"{devices_warning} `num_processes={self.num_processes}`")
def _set_devices_if_none(self) -> None:
if self.devices is not None:
return
if self._accelerator_type == DeviceType.TPU:
self.devices = self.tpu_cores
elif self._accelerator_type == DeviceType.IPU:
self.devices = self.ipus
elif self._accelerator_type == DeviceType.GPU:
self.devices = self.gpus
elif self._accelerator_type == DeviceType.CPU:
self.devices = self.num_processes
def handle_given_plugins(self) -> None:
training_type = None
checkpoint = None
precision = None
cluster_environment = None
for plug in self.plugins:
if isinstance(plug, str) and plug in TrainingTypePluginsRegistry:
if training_type is None:
training_type = TrainingTypePluginsRegistry.get(plug)
else:
raise MisconfigurationException(
"You can only specify one precision and one training type plugin."
" Found more than 1 training type plugin:"
f' {TrainingTypePluginsRegistry[plug]["plugin"]} registered to {plug}'
)
if isinstance(plug, str):
# Reset the distributed type as the user has overridden training type
# via the plugins argument
self._distrib_type = None
self.set_distributed_mode(plug)
elif isinstance(plug, TrainingTypePlugin):
if training_type is None:
training_type = plug
else:
raise MisconfigurationException(
"You can only specify one training type plugin."
f" Available: {type(training_type).__name__}, given: {type(plug).__name__}"
)
elif isinstance(plug, PrecisionPlugin):
if precision is None:
precision = plug
else:
raise MisconfigurationException(
"You can only specify one precision plugin."
f" Available: {type(precision).__name__}, given: {type(plug).__name__}"
)
elif isinstance(plug, CheckpointIO):
if checkpoint is None:
checkpoint = plug
else:
raise MisconfigurationException(
"You can only specify one checkpoint plugin."
f" Available: {type(checkpoint).__name__}, given: {type(plug).__name__}"
)
elif isinstance(plug, ClusterEnvironment):
if cluster_environment is None:
cluster_environment = plug
else:
raise MisconfigurationException(
"You can only specify one cluster environment. Found more than 1 cluster environment plugin"
)
else:
raise MisconfigurationException(
f"Found invalid type for plugin {plug}. Expected a precision or training type plugin."
)
self._training_type_plugin = training_type
self._precision_plugin = precision
self._checkpoint_io = checkpoint
self._cluster_environment = cluster_environment or self.select_cluster_environment(
)
@property
def precision_plugin(self) -> PrecisionPlugin:
if self._precision_plugin is None:
self._precision_plugin = self.select_precision_plugin()
return self._precision_plugin
@property
def training_type_plugin(self) -> TrainingTypePlugin:
if self._training_type_plugin_resolved:
# avoid calling `resolve_training_type_plugin` multiple times
return self._training_type_plugin
if self._training_type_plugin is None:
self._training_type_plugin = self.select_training_type_plugin()
self._training_type_plugin = self.resolve_training_type_plugin(
self._training_type_plugin)
# attach checkpoint plugin to the training type plugin
if self._checkpoint_io is not None:
self._training_type_plugin.checkpoint_io = self._checkpoint_io
self._training_type_plugin_resolved = True
return self._training_type_plugin
@property
def cluster_environment(self) -> ClusterEnvironment:
if self._cluster_environment is None:
self._cluster_environment = self.select_cluster_environment()
return self._cluster_environment
@property
def has_cpu(self) -> bool:
return True
@property
def use_cpu(self) -> bool:
return self._accelerator_type == DeviceType.CPU
@property
def has_gpu(self) -> bool:
# Here, we are not checking for GPU availability, but instead if User has passed
# `gpus` to Trainer for training.
gpus = self.parallel_device_ids
if gpus is not None and len(gpus) > 0:
return True
return self._map_devices_to_accelerator(DeviceType.GPU)
@property
def use_gpu(self) -> bool:
return self._accelerator_type == DeviceType.GPU and self.has_gpu
@property
def has_tpu(self) -> bool:
# Here, we are not checking for TPU availability, but instead if User has passed
# `tpu_cores` to Trainer for training.
if self.tpu_cores is not None:
return True
return self._map_devices_to_accelerator(DeviceType.TPU)
@property
def use_tpu(self) -> bool:
return self._accelerator_type == DeviceType.TPU and self.has_tpu
@property
def tpu_id(self) -> Optional[int]:
if self.use_tpu and isinstance(self.tpu_cores, list):
return self.tpu_cores[0]
return None
@property
def has_ipu(self) -> bool:
# Here, we are not checking for IPU availability, but instead if User has passed
# `ipus` to Trainer for training.
if self.ipus is not None or isinstance(self._training_type_plugin,
IPUPlugin):
return True
return self._map_devices_to_accelerator(DeviceType.IPU)
@property
def use_ipu(self) -> bool:
return self._accelerator_type == DeviceType.IPU and self.has_ipu
def _set_devices_to_cpu_num_processes(self) -> None:
if self.num_processes == 1:
self._map_devices_to_accelerator(DeviceType.CPU)
def _map_devices_to_accelerator(self, accelerator: str) -> bool:
if self.devices is None:
return False
if accelerator == DeviceType.TPU and _TPU_AVAILABLE:
self.tpu_cores = device_parser.parse_tpu_cores(self.devices)
return True
if accelerator == DeviceType.IPU and _IPU_AVAILABLE:
self.ipus = self.devices
return True
if accelerator == DeviceType.GPU and torch.cuda.is_available():
self.gpus = self.devices
self.parallel_device_ids = device_parser.parse_gpu_ids(
self.devices)
return True
if accelerator == DeviceType.CPU:
if not isinstance(self.devices, int):
raise MisconfigurationException(
"The flag `devices` only supports integer for `accelerator='cpu'`,"
f" got `devices={self.devices}` instead.")
self.num_processes = self.devices
return True
return False
@property
def use_dp(self) -> bool:
return self._distrib_type == DistributedType.DP
@property
def use_ddp(self) -> bool:
return self._distrib_type in (
DistributedType.DDP,
DistributedType.DDP_SPAWN,
DistributedType.DDP_SHARDED,
DistributedType.DDP_SHARDED_SPAWN,
DistributedType.DDP_FULLY_SHARDED,
DistributedType.DEEPSPEED,
DistributedType.TPU_SPAWN,
)
@property
def use_ddp2(self) -> bool:
return self._distrib_type == DistributedType.DDP2
@property
def use_horovod(self) -> bool:
return self._distrib_type == DistributedType.HOROVOD
@property
def use_deepspeed(self) -> bool:
return self._distrib_type == DistributedType.DEEPSPEED
@property
def _is_sharded_training_type(self) -> bool:
return isinstance(self.training_type_plugin,
(DDPShardedPlugin, DDPSpawnShardedPlugin))
@property
def _is_fully_sharded_training_type(self) -> bool:
return isinstance(self.training_type_plugin, DDPFullyShardedPlugin)
@property
def is_distributed(self) -> bool:
# Used for custom plugins.
# Custom plugins should implement is_distributed property.
if hasattr(self.training_type_plugin,
"is_distributed") and not self.use_tpu:
return self.training_type_plugin.is_distributed
is_distributed = self.use_ddp or self.use_ddp2 or self.use_horovod
if self.use_tpu:
is_distributed |= self.training_type_plugin.is_distributed
return is_distributed
@property
def num_gpus(self) -> int:
gpus = self.parallel_device_ids
if gpus is None:
return 0
return len(gpus)
@property
def num_ipus(self) -> int:
if isinstance(self.ipus, int):
return self.ipus
if isinstance(self._training_type_plugin, IPUPlugin):
return self._training_type_plugin.replication_factor
return 0
@property
def parallel_devices(self) -> List[Union[torch.device, int]]:
if self.use_gpu:
devices = [
torch.device("cuda", i) for i in self.parallel_device_ids
]
elif self.use_tpu:
# explicitly don't make a tpu device here!
# https://github.com/PyTorchLightning/pytorch-lightning/issues/3169
if isinstance(self.tpu_cores, int):
devices = list(range(self.tpu_cores))
elif self.use_ipu:
devices = list(range(self.num_ipus))
else:
devices = [torch.device("cpu")] * self.num_processes
return devices
@property
def root_gpu(self) -> Optional[int]:
return (self.accelerator.root_device.index
if not isinstance(self.accelerator,
(IPUAccelerator, TPUAccelerator)) else None)
@property
def is_training_type_in_plugins(self) -> bool:
return any(
isinstance(plug, str) and plug in TrainingTypePluginsRegistry
for plug in self.plugins)
@property
def is_using_torchelastic(self) -> bool:
"""
.. deprecated:: v1.3
Will be removed in v1.5.0.
Returns:
``True`` if the current process was launched using the torchelastic command.
"""
rank_zero_deprecation(
"The property `AcceleratorConnector.is_using_torchelastic` was deprecated in v1.3"
" and will be removed in 1.5. Use `TorchElasticEnvironment.is_using_torchelastic()` instead."
)
return TorchElasticEnvironment.is_using_torchelastic()
def select_precision_plugin(self) -> PrecisionPlugin:
# set precision type
self.amp_type = AMPType.from_str(self.amp_type)
if self.use_ipu:
return IPUPrecisionPlugin(self.precision)
if self._distrib_type == DistributedType.DEEPSPEED or isinstance(
self._training_type_plugin, DeepSpeedPlugin):
return DeepSpeedPrecisionPlugin(self.precision)
if self.precision == 32:
return PrecisionPlugin()
if self.precision == 64:
return DoublePrecisionPlugin()
if self.precision in (16, "bf16"):
if self.use_tpu:
return TPUHalfPrecisionPlugin()
if self.amp_type == AMPType.NATIVE:
if not _NATIVE_AMP_AVAILABLE:
msg = (
"You have asked for native AMP but your PyTorch version does not support it."
" Consider upgrading with `pip install torch>=1.6`.")
if _APEX_AVAILABLE:
self.amp_type = AMPType.APEX
msg += " We will attempt to use NVIDIA Apex for this session."
rank_zero_warn(msg)
else:
raise MisconfigurationException(msg)
else:
log.info(
f"Using native {self.precision} bit Automatic Mixed Precision"
)
if self._is_sharded_training_type:
return ShardedNativeMixedPrecisionPlugin(
self.precision, use_cpu=self.use_cpu)
if self._is_fully_sharded_training_type:
return FullyShardedNativeMixedPrecisionPlugin(
self.precision, use_cpu=self.use_cpu)
return NativeMixedPrecisionPlugin(self.precision,
use_cpu=self.use_cpu)
if self.amp_type == AMPType.APEX:
if not _APEX_AVAILABLE:
raise MisconfigurationException(
"You have asked for Apex AMP but you have not installed it yet."
" Install apex first using this guide: https://github.com/NVIDIA/apex#linux"
)
if self._is_sharded_training_type or self._is_fully_sharded_training_type:
raise MisconfigurationException(
"Sharded Plugin is not supported with Apex AMP, please using native AMP for 16-bit precision."
)
log.info("Using APEX 16bit precision.")
return ApexMixedPrecisionPlugin(self.amp_level)
raise MisconfigurationException(
f"Precision {self.precision} is invalid. Allowed precision values: {PrecisionType.supported_types()}"
)
def select_training_type_plugin(self) -> TrainingTypePlugin:
if (isinstance(self.distributed_backend, Accelerator)
and self.distributed_backend.training_type_plugin is not None):
plugin = self.distributed_backend.training_type_plugin
elif self.use_ddp2:
plugin = DDP2Plugin(parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment)
elif self.use_ddp and self.use_deepspeed:
plugin = DeepSpeedPlugin(
cluster_environment=self.select_cluster_environment(),
parallel_devices=self.parallel_devices)
elif self.use_ddp:
use_slurm_ddp = self.use_ddp and self.is_slurm_managing_tasks
use_torchelastic_ddp = self.use_ddp and TorchElasticEnvironment.is_using_torchelastic(
)
use_kubeflow_ddp = self.use_ddp and KubeflowEnvironment.is_using_kubeflow(
)
use_ddp_spawn = self._distrib_type == DistributedType.DDP_SPAWN
use_ddp_cpu_spawn = use_ddp_spawn and self.use_cpu
use_tpu_spawn = self.use_tpu and self._distrib_type == DistributedType.TPU_SPAWN
use_ddp_cpu_torch_elastic = use_ddp_cpu_spawn and TorchElasticEnvironment.is_using_torchelastic(
)
use_ddp_cpu_kubeflow = use_ddp_cpu_spawn and KubeflowEnvironment.is_using_kubeflow(
)
use_ddp_cpu_slurm = use_ddp_cpu_spawn and self.is_slurm_managing_tasks
use_ddp_sharded = self._distrib_type == DistributedType.DDP_SHARDED
use_ddp_sharded_spawn = self._distrib_type == DistributedType.DDP_SHARDED_SPAWN
use_ddp_fully_sharded = self._distrib_type == DistributedType.DDP_FULLY_SHARDED
if use_tpu_spawn:
ddp_plugin_cls = TPUSpawnPlugin
elif use_ddp_sharded:
ddp_plugin_cls = DDPShardedPlugin
elif use_ddp_sharded_spawn:
ddp_plugin_cls = DDPSpawnShardedPlugin
elif (use_ddp_cpu_slurm or use_slurm_ddp
or use_ddp_cpu_torch_elastic or use_torchelastic_ddp
or use_kubeflow_ddp or use_ddp_cpu_kubeflow):
ddp_plugin_cls = DDPPlugin
elif use_ddp_spawn or use_ddp_cpu_spawn:
ddp_plugin_cls = DDPSpawnPlugin
elif use_ddp_fully_sharded:
ddp_plugin_cls = DDPFullyShardedPlugin
else:
ddp_plugin_cls = DDPPlugin
plugin = ddp_plugin_cls(
parallel_devices=self.parallel_devices,
cluster_environment=self.cluster_environment)
elif self.use_dp:
plugin = DataParallelPlugin(parallel_devices=self.parallel_devices)
elif self.use_horovod:
plugin = HorovodPlugin(parallel_devices=self.parallel_devices)
elif self.use_tpu and isinstance(self.tpu_cores, list):
plugin = SingleTPUPlugin(self.tpu_id)
elif self.use_ipu:
plugin = IPUPlugin(parallel_devices=self.parallel_devices)
else:
single_gpu_ordinal = device_parser.determine_root_gpu_device(
self.parallel_device_ids)
plugin = SingleDevicePlugin(device=torch.device(
f"cuda:{single_gpu_ordinal}" if self.use_gpu else "cpu"))
return plugin
def resolve_training_type_plugin(
self, training_type: TrainingTypePlugin) -> TrainingTypePlugin:
# necessary for when the user has passed in a plugin
if hasattr(training_type, "parallel_devices") and getattr(
training_type, "parallel_devices") is None:
training_type.parallel_devices = self.parallel_devices
if hasattr(training_type, "num_processes"):
training_type.num_processes = len(self.parallel_devices)
if hasattr(training_type, "cluster_environment") and getattr(
training_type, "cluster_environment") is None:
# transfer ownership of the cluster environment to the training type
training_type.cluster_environment = self.cluster_environment
self._cluster_environment = proxy(self.cluster_environment)
if hasattr(training_type, "num_nodes"):
# set num_nodes for training_type from trainer setting
training_type.num_nodes = self.num_nodes
if hasattr(training_type, "sync_batchnorm"):
# set sync_batchnorm for training_type from trainer setting
training_type.sync_batchnorm = self.sync_batchnorm
return training_type
def select_accelerator(self) -> Accelerator:
if isinstance(self.distributed_backend, Accelerator):
# custom accelerator from user
if self._precision_plugin is not None or self._training_type_plugin is not None:
# plugins also specified by user
rank_zero_warn(
"Specified `Precision` and `TrainingType` plugins will be ignored,"
" since an `Accelerator` instance was provided.")
return self.distributed_backend
if self.use_gpu:
acc_cls = GPUAccelerator
elif self.use_tpu:
acc_cls = TPUAccelerator
elif self.use_ipu:
acc_cls = IPUAccelerator
else:
acc_cls = CPUAccelerator
# as precision_plugin is dependent on training_type_plugin, make sure
# that we first select training_type_plugin, then precision_plugin
accelerator = acc_cls(training_type_plugin=self.training_type_plugin,
precision_plugin=self.precision_plugin)
# transfer ownership of the plugins to the accelerator
self._training_type_plugin = proxy(self.training_type_plugin)
self._precision_plugin = proxy(self.precision_plugin)
return accelerator
def select_cluster_environment(self) -> ClusterEnvironment:
if self._cluster_environment is not None:
return self._cluster_environment
if self.is_slurm_managing_tasks:
env = SLURMEnvironment()
elif TorchElasticEnvironment.is_using_torchelastic():
env = TorchElasticEnvironment()
elif KubeflowEnvironment.is_using_kubeflow():
env = KubeflowEnvironment()
elif LSFEnvironment.is_using_lsf():
env = LSFEnvironment()
else:
env = LightningEnvironment()
return env
def set_distributed_mode(self, distributed_backend: Optional[str] = None):
if distributed_backend is None and self.is_training_type_in_plugins:
return
if distributed_backend is not None and distributed_backend in TrainingTypePluginsRegistry:
self.distributed_backend = TrainingTypePluginsRegistry[
distributed_backend]["distributed_backend"]
elif distributed_backend is not None:
self.distributed_backend = distributed_backend
if isinstance(self.distributed_backend, Accelerator):
return
is_cpu_accelerator_type = self._accelerator_type and self._accelerator_type == DeviceType.CPU
_use_cpu = is_cpu_accelerator_type or self.distributed_backend and "cpu" in self.distributed_backend
if self.distributed_backend is None:
if self.has_horovodrun():
self._set_horovod_backend()
elif self.num_gpus == 0 and self.num_nodes > 1:
self._distrib_type = DistributedType.DDP
elif self.num_gpus == 0 and self.num_processes > 1:
self.distributed_backend = DistributedType.DDP_SPAWN
elif self.num_gpus > 1 and not _use_cpu:
rank_zero_warn(
"You requested multiple GPUs but did not specify a backend, e.g."
' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
)
self.distributed_backend = DistributedType.DDP_SPAWN
# special case with DDP on CPUs
if self.distributed_backend == DistributedType.DDP_CPU:
if _TPU_AVAILABLE:
raise MisconfigurationException(
"`accelerator='ddp_cpu'` is not supported on TPU machines. "
"Learn more: https://github.com/PyTorchLightning/pytorch-lightning/issues/7810"
)
self._distrib_type = DistributedType.DDP_SPAWN
if self.num_gpus > 0:
rank_zero_warn(
"You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs."
)
self.parallel_device_ids = None
if self.num_processes is None:
# define the max CPU available
self.num_processes = os.cpu_count()
# special case with TPUs
elif self.has_tpu and not _use_cpu:
self._device_type = DeviceType.TPU
if isinstance(self.tpu_cores, int):
self._distrib_type = DistributedType.TPU_SPAWN
elif self.has_ipu and not _use_cpu:
self._device_type = DeviceType.IPU
elif self.distributed_backend and self._distrib_type is None:
self._distrib_type = DistributedType(self.distributed_backend)
if self.num_gpus > 0 and not _use_cpu:
self._device_type = DeviceType.GPU
_gpu_distrib_types = (DistributedType.DP, DistributedType.DDP,
DistributedType.DDP_SPAWN, DistributedType.DDP2)
# DP and DDP2 cannot run without GPU
if self.num_gpus == 0 and self._distrib_type in _gpu_distrib_types and not _use_cpu:
if (self.num_nodes
and self.num_nodes > 1) or (self.num_processes
and self.num_processes > 1):
if self._distrib_type in (DistributedType.DP,
DistributedType.DDP2):
rank_zero_warn(
f"{self._distrib_type} is not supported on CPUs, hence setting the distributed type to `ddp`."
)
self._distrib_type = DistributedType.DDP
else:
rank_zero_warn(
"You are running on single node with no parallelization, so distributed has no effect."
)
self._distrib_type = None
# finished configuring self._distrib_type, check ipython environment
self.check_interactive_compatibility()
# for DDP overwrite nb processes by requested GPUs
if self._device_type == DeviceType.GPU and self._distrib_type in (
DistributedType.DDP,
DistributedType.DDP_SPAWN,
):
self.num_processes = self.num_gpus
if self._device_type == DeviceType.GPU and self._distrib_type == DistributedType.DDP2:
self.num_processes = self.num_nodes
# Horovod is an extra case...
if self.distributed_backend == DistributedType.HOROVOD:
self._set_horovod_backend()
using_valid_distributed = self.use_ddp or self.use_ddp2
if self.num_nodes > 1 and not using_valid_distributed:
# throw error to force user to choose a supported distributed type such as ddp or ddp2
raise MisconfigurationException(
"Your chosen distributed type does not support num_nodes > 1. "
"Please set accelerator=ddp or accelerator=ddp2.")
def _set_horovod_backend(self):
self.check_horovod()
self._distrib_type = DistributedType.HOROVOD
# Initialize Horovod to get rank / size info
hvd.init()
if self.has_gpu:
# Horovod assigns one local GPU per process
self.parallel_device_ids = list(range(hvd.local_size()))
else:
self.num_processes = hvd.local_size()
def check_interactive_compatibility(self):
"""Raises a `MisconfigurationException` if the accelerator and/or plugin is not compatible with an
interactive environment."""
from pytorch_lightning.utilities import _IS_INTERACTIVE
if _IS_INTERACTIVE and self._distrib_type is not None and not self._distrib_type.is_interactive_compatible(
):
raise MisconfigurationException(
f"Selected distributed backend {self._distrib_type} is not compatible with an interactive"
" environment. Run your code as a script, or choose one of the compatible backends:"
f" {', '.join(DistributedType.interactive_compatible_types())}"
)
def check_horovod(self):
"""Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
if not _HOROVOD_AVAILABLE:
raise MisconfigurationException(
'Requested `accelerator="horovod"`, but Horovod is not installed.'
"Install with \n $HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]"
)
if self.num_gpus > 1 or self.num_nodes > 1:
raise MisconfigurationException(
"Horovod does not support setting num_nodes / num_gpus explicitly. Use "
"horovodrun / mpirun to configure the number of processes.")
@staticmethod
def has_horovodrun() -> bool:
"""Returns True if running with `horovodrun` using Gloo or OpenMPI."""
return _HOROVOD_AVAILABLE and ("OMPI_COMM_WORLD_RANK" in os.environ
or "HOROVOD_RANK" in os.environ)
def update_device_type_if_ipu_plugin(self) -> None:
# This allows the poptorch.Options that are passed into the IPUPlugin to be the source of truth,
# which gives users the flexibility to not have to pass `ipus` flag directly to Trainer
if isinstance(self._training_type_plugin,
IPUPlugin) and self._device_type != DeviceType.IPU:
self._device_type = DeviceType.IPU
def configure_slurm_ddp(self):
# extract SLURM flag vars
# whenever we have the correct number of tasks, we let slurm manage processes
# otherwise we launch the required number of processes
if self.use_ddp or self.use_ddp2:
num_requested_gpus = self.num_gpus * self.num_nodes
num_slurm_tasks = 0
try:
num_slurm_tasks = int(os.environ["SLURM_NTASKS"])
self.is_slurm_managing_tasks = num_slurm_tasks == num_requested_gpus
# enable slurm cpu
if num_requested_gpus == 0:
self.is_slurm_managing_tasks = num_slurm_tasks == self.num_processes
# in interactive mode we don't manage tasks
job_name = os.environ["SLURM_JOB_NAME"]
if job_name == "bash":
self.is_slurm_managing_tasks = False
except Exception:
# likely not on slurm, so set the slurm managed flag to false
self.is_slurm_managing_tasks = False
# used for tests only, set this flag to simulate slurm managing a task
try:
should_fake = int(os.environ["FAKE_SLURM_MANAGING_TASKS"])
if should_fake:
self.is_slurm_managing_tasks = True
except Exception:
pass
# notify user the that slurm is managing tasks
if self.is_slurm_managing_tasks:
rank_zero_info("Multi-processing is handled by Slurm.")
def set_distributed_mode(self):
if self.distributed_backend is None:
if self.has_horovodrun():
self._set_horovod_backend()
elif self.num_gpus == 0 and (self.num_nodes > 1
or self.num_processes > 1):
self._distrib_type = DistributedType.DDP
elif self.num_gpus > 1:
rank_zero_warn(
'You requested multiple GPUs but did not specify a backend, e.g.'
' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
)
self.distributed_backend = "ddp_spawn"
# special case with DDP on CPUs
if self.distributed_backend == "ddp_cpu":
self._distrib_type = DistributedType.DDP
self.data_parallel_device_ids = None
if self.num_gpus > 0:
rank_zero_warn(
'You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs.'
)
if self.num_processes is None:
# define the max CPU available
self.num_processes = os.cpu_count()
# special case with TPUs
elif self.distributed_backend == 'tpu':
self._device_type = DeviceType.TPU
# set all other requested distrib. types adn if it was not set in the
elif self.distributed_backend and self._distrib_type is None:
self._distrib_type = DistributedType(self.distributed_backend)
# unless you request explicitly for CPU and some GPU are available use them
_on_cpu = self.distributed_backend and 'cpu' in self.distributed_backend
if (self.num_gpus > 0 and not _on_cpu):
self._device_type = DeviceType.GPU
_distrib_types = (DistributedType.DP, DistributedType.DDP,
DistributedType.DDP_SPAWN, DistributedType.DDP2)
# DP and DDP2 cannot run without GPU
if (self.num_gpus == 0 and self._distrib_type in _distrib_types):
rank_zero_warn(
'You requested distributed training on GPUs, but none is available, so we set backend to `ddp_cpu`.'
)
# todo: in some cases it yield in comarison None and int
if ((self.num_nodes and self.num_nodes > 1)
or (self.num_processes and self.num_processes > 1)):
self._distrib_type = DistributedType.DDP
else:
rank_zero_warn(
'You are running on single node with no parallelization, so distributed has no effect.'
)
self._distrib_type = None
# for DDP overwrite nb processes by requested GPUs
if (self._device_type == DeviceType.GPU and self._distrib_type
in (DistributedType.DDP, DistributedType.DDP_SPAWN)):
self.num_processes = self.num_gpus
# Horovod is an extra case...
if self.distributed_backend == "horovod":
self._set_horovod_backend()
# throw error to force user ddp or ddp2 choice
_ddp = (DistributedType.DDP, DistributedType.DDP_SPAWN,
DistributedType.DDP2)
if (self.num_nodes > 1 and self._distrib_type not in _ddp):
raise MisconfigurationException(
'DataParallel does not support num_nodes > 1. Switching to DistributedDataParallel for you. '
'To silence this warning set `accelerator="ddp"` or `accelerator="ddp2"`'
)
rank_zero_info(
f'GPU available: {torch.cuda.is_available()}, used: {self._device_type == DeviceType.GPU}'
)
num_cores = self.tpu_cores if self.tpu_cores is not None else 0
rank_zero_info(
f'TPU available: {_TPU_AVAILABLE}, using: {num_cores} TPU cores')
if torch.cuda.is_available() and self._device_type != DeviceType.GPU:
rank_zero_warn(
"GPU available but not used. Set the --gpus flag when calling the script."
)
