473 lines
18 KiB
Python
473 lines
18 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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from pytorch_lightning import _logger as log
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from pytorch_lightning.accelerators.accelerator import Accelerator
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from pytorch_lightning.accelerators.cpu import CPUAccelerator
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from pytorch_lightning.accelerators.gpu import GPUAccelerator
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from pytorch_lightning.accelerators.tpu import TPUAccelerator
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from pytorch_lightning.plugins import (
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ApexMixedPrecisionPlugin,
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DataParallelPlugin,
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DDP2Plugin,
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DDPPlugin,
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DDPSpawnPlugin,
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HorovodPlugin,
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NativeMixedPrecisionPlugin,
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PrecisionPlugin,
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ShardedNativeMixedPrecisionPlugin,
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SingleDevicePlugin,
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SingleTPUPlugin,
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TPUHalfPrecisionPlugin,
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TPUSpawnPlugin,
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)
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from pytorch_lightning.plugins.environments import SLURMEnvironment, TorchElasticEnvironment
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from pytorch_lightning.tuner.auto_gpu_select import pick_multiple_gpus
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from pytorch_lightning.utilities import (
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_APEX_AVAILABLE,
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_HOROVOD_AVAILABLE,
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_NATIVE_AMP_AVAILABLE,
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_TPU_AVAILABLE,
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AMPType,
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device_parser,
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DeviceType,
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DistributedType,
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rank_zero_only,
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)
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from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_warn
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _HOROVOD_AVAILABLE:
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import horovod.torch as hvd
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class BackendConnector(object):
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def __init__(
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self,
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num_processes,
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tpu_cores,
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distributed_backend,
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auto_select_gpus,
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gpus,
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num_nodes,
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sync_batchnorm,
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benchmark,
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replace_sampler_ddp,
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deterministic,
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precision,
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amp_type,
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amp_level,
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cluster_environment,
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):
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# initialization
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self._device_type = DeviceType.CPU
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self._distrib_type = None
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self.num_processes = num_processes
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self.tpu_cores = device_parser.parse_tpu_cores(tpu_cores)
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self.distributed_backend = distributed_backend
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self.auto_select_gpus = auto_select_gpus
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self.gpus = gpus
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self.num_nodes = num_nodes
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self.sync_batchnorm = sync_batchnorm
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self.benchmark = benchmark
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self.replace_sampler_ddp = replace_sampler_ddp
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self.deterministic = deterministic
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self.precision = precision
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self.amp_type = amp_type.lower() if isinstance(amp_type, str) else None
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self.amp_level = amp_level
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self.cluster_environment = cluster_environment
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self.is_slurm_managing_tasks = False
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# init the default rank if exists
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# we need to call this here or NVIDIA flags and other messaging in init will show on all ranks
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# this way we only show it on rank 0
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if "LOCAL_RANK" in os.environ:
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rank_zero_only.rank = int(os.environ["LOCAL_RANK"])
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# for gpus allow int, string and gpu list
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if auto_select_gpus and isinstance(gpus, int):
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self.gpus = pick_multiple_gpus(gpus)
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self.parallel_device_ids = device_parser.parse_gpu_ids(self.gpus)
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self.root_gpu = device_parser.determine_root_gpu_device(self.parallel_device_ids)
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self.set_distributed_mode()
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self.configure_slurm_ddp()
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self.accelerator = self.select_accelerator()
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# override dist backend when using tpus
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if self.on_tpu:
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self.distributed_backend = "tpu"
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self.use_tpu = True
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# init flags for SLURM+DDP to work
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self.world_size = 1
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self.interactive_ddp_procs = []
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self.global_rank = 0
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# NVIDIA setup
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# self.set_nvidia_flags(self.trainer.is_slurm_managing_tasks, self.trainer.data_parallel_device_ids)
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# benchmarking
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# TODO: should this be moved to GPU accelerator?
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torch.backends.cudnn.benchmark = self.benchmark
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# determinism for cudnn
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# TODO: should this be moved to GPU accelerator?
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torch.backends.cudnn.deterministic = deterministic
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if deterministic:
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# fixing non-deterministic part of horovod
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# https://github.com/PyTorchLightning/pytorch-lightning/pull/1572/files#r420279383
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os.environ["HOROVOD_FUSION_THRESHOLD"] = str(0)
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# TODO: move this to TPU accelerator/plugin
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self.on_colab_kaggle = os.getenv("COLAB_GPU") or os.getenv("KAGGLE_URL_BASE")
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self.replace_sampler_ddp = replace_sampler_ddp
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@property
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def on_cpu(self):
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return self._device_type == DeviceType.CPU
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@property
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def on_tpu(self):
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return self.tpu_cores is not None
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@property
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def tpu_id(self):
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if self.on_tpu:
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return self.tpu_cores[0]
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return None
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@property
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def on_gpu(self):
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gpus = self.parallel_device_ids
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return gpus is not None and len(gpus) > 0 and torch.cuda.is_available()
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@property
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def use_dp(self):
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return self._distrib_type == DistributedType.DP
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@property
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def use_ddp(self):
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return self._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN)
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@property
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def use_ddp2(self):
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return self._distrib_type == DistributedType.DDP2
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@property
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def use_horovod(self):
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return self._distrib_type == DistributedType.HOROVOD
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@property
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def num_gpus(self) -> int:
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gpus = self.parallel_device_ids
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if gpus is None:
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return 0
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return len(gpus)
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@property
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def parallel_devices(self):
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if self.on_gpu:
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devices = [torch.device("cuda", i) for i in self.parallel_device_ids]
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elif self.on_tpu:
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# explicitly don't make a tpu device here!
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# https://github.com/PyTorchLightning/pytorch-lightning/issues/3169
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devices = [i for i in self.parallel_device_ids]
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else:
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devices = [torch.device("cpu")] * self.num_processes
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return devices
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@property
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def is_using_torchelastic(self):
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te_flags_passed = "WORLD_SIZE" in os.environ and ("GROUP_RANK" in os.environ or "NODE_RANK" in os.environ)
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return te_flags_passed
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def select_precision_plugin(self):
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if self.precision == 32:
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self.amp_type = None
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return PrecisionPlugin()
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elif self.precision == 16:
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if self.on_tpu:
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return TPUHalfPrecisionPlugin()
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if self.amp_type == "native":
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if not _NATIVE_AMP_AVAILABLE:
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rank_zero_warn(
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"You have asked for native AMP but your PyTorch version does not support it."
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" Consider upgrading with `pip install torch>=1.6`."
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" We will attempt to use NVIDIA Apex for this session."
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)
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self.amp_type = "apex"
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else:
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log.info("Using native 16bit precision.")
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if self.distributed_backend == "ddp_sharded" or self.distributed_backend == "ddp_sharded_spawn":
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return ShardedNativeMixedPrecisionPlugin()
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self.amp_type = AMPType.NATIVE
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return NativeMixedPrecisionPlugin()
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if self.amp_type == "apex":
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if not _APEX_AVAILABLE:
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rank_zero_warn(
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"You have asked for Apex AMP but you have not installed it yet."
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" Install apex first using this guide: https://github.com/NVIDIA/apex#linux"
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)
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else:
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if self.distributed_backend == "ddp_sharded" or self.distributed_backend == "ddp_sharded_spawn":
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raise MisconfigurationException(
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"Sharded Plugin is not supported with Apex AMP, "
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"please using native AMP for 16-bit precision."
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)
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log.info("Using APEX 16bit precision.")
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self.amp_type = AMPType.APEX
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return ApexMixedPrecisionPlugin(self.amp_level)
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else:
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raise NotImplementedError("We only support precisions 32 and 16!")
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def select_training_type_plugin(self):
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cluster_environment = self.select_cluster_environment()
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if self.use_ddp2:
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plugin = DDP2Plugin(parallel_devices=self.parallel_devices, cluster_environment=cluster_environment)
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elif self.use_ddp:
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use_slurm_ddp = self.use_ddp and self.is_slurm_managing_tasks
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use_torchelastic_ddp = self.use_ddp and self.is_using_torchelastic
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use_ddp_spawn = self._distrib_type == DistributedType.DDP_SPAWN
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use_ddp_cpu_spawn = self.use_ddp and self.on_cpu
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use_ddp_cpu_torch_elastic = use_ddp_cpu_spawn and self.is_using_torchelastic
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use_ddp_cpu_slurm = use_ddp_cpu_spawn and self.is_slurm_managing_tasks
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# use_ddp_sharded = self.distributed_backend == "ddp_sharded"
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# use_ddp_sharded_spawn = self.distributed_backend == "ddp_sharded_spawn"
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if self.on_tpu:
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ddp_plugin_cls = TPUSpawnPlugin
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# ddp script mode uses the same flags as TE
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# TODO: decouple from TE
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if os.environ.get("PL_IN_DDP_SUBPROCESS", False):
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use_torchelastic_ddp = False
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# fixme
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# if use_ddp_sharded:
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# ddp_plugin_cls = DDPShardedPlugin
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# elif use_ddp_sharded_spawn:
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# ddp_plugin_cls = DDPSpawnShardedPlugin
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if use_ddp_cpu_slurm or use_slurm_ddp or use_ddp_cpu_torch_elastic or use_torchelastic_ddp:
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ddp_plugin_cls = DDPPlugin
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elif use_ddp_spawn or use_ddp_cpu_spawn:
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ddp_plugin_cls = DDPSpawnPlugin
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else:
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ddp_plugin_cls = DDPPlugin
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plugin = ddp_plugin_cls(
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parallel_devices=self.parallel_devices,
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num_nodes=self.num_nodes,
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cluster_environment=cluster_environment,
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sync_batchnorm=self.sync_batchnorm,
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)
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elif self.use_dp:
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plugin = DataParallelPlugin(parallel_devices=self.parallel_devices)
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elif self.use_horovod:
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plugin = HorovodPlugin(parallel_devices=self.parallel_devices)
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elif self.on_tpu:
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plugin = SingleTPUPlugin(self.tpu_id)
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else:
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plugin = SingleDevicePlugin(device=torch.device(f"cuda:{self.root_gpu}" if self.on_gpu else "cpu"))
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return plugin
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def select_accelerator(self):
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if isinstance(self.distributed_backend, Accelerator):
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# custom accelerator from user
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return self.distributed_backend
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if self.on_gpu:
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acc_cls = GPUAccelerator
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elif self.on_tpu:
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acc_cls = TPUAccelerator
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else:
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acc_cls = CPUAccelerator
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return acc_cls(
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precision_plugin=self.select_precision_plugin(),
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training_type_plugin=self.select_training_type_plugin(),
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)
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def select_cluster_environment(self):
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if self.cluster_environment is not None:
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return self.cluster_environment
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if self.is_slurm_managing_tasks:
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env = SLURMEnvironment()
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elif self.is_using_torchelastic:
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env = TorchElasticEnvironment()
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# TODO: decouple DDP from TE
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# maybe introduce a DefaultEnvironment?
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os.environ["PL_IN_DDP_SUBPROCESS"] = "1"
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else:
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# TODO: maybe introduce a DefaultEnvironment?
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env = TorchElasticEnvironment()
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return env
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def set_distributed_mode(self):
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if self.distributed_backend is None:
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if self.has_horovodrun():
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self._set_horovod_backend()
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elif self.num_gpus == 0 and (self.num_nodes > 1 or self.num_processes > 1):
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self._distrib_type = DistributedType.DDP
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elif self.num_gpus > 1:
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rank_zero_warn(
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'You requested multiple GPUs but did not specify a backend, e.g.'
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' `Trainer(accelerator="dp"|"ddp"|"ddp2")`. Setting `accelerator="ddp_spawn"` for you.'
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)
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self.distributed_backend = "ddp_spawn"
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# special case with DDP on CPUs
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if self.distributed_backend == "ddp_cpu":
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self._distrib_type = DistributedType.DDP
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self.data_parallel_device_ids = None
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if self.num_gpus > 0:
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rank_zero_warn(
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'You requested one or more GPUs, but set the backend to `ddp_cpu`. Training will not use GPUs.'
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)
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if self.num_processes is None:
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# define the max CPU available
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self.num_processes = os.cpu_count()
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# special case with TPUs
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elif self.distributed_backend == 'tpu':
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self._device_type = DeviceType.TPU
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# set all other requested distrib. types adn if it was not set in the
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elif self.distributed_backend and self._distrib_type is None:
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self._distrib_type = DistributedType(self.distributed_backend)
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# unless you request explicitly for CPU and some GPU are available use them
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_on_cpu = self.distributed_backend and 'cpu' in self.distributed_backend
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if (self.num_gpus > 0 and not _on_cpu):
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self._device_type = DeviceType.GPU
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_distrib_types = (DistributedType.DP, DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2)
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# DP and DDP2 cannot run without GPU
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if (self.num_gpus == 0 and self._distrib_type in _distrib_types):
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rank_zero_warn(
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'You requested distributed training on GPUs, but none is available, so we set backend to `ddp_cpu`.'
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)
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# todo: in some cases it yield in comarison None and int
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if ((self.num_nodes and self.num_nodes > 1) or (self.num_processes and self.num_processes > 1)):
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self._distrib_type = DistributedType.DDP
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else:
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rank_zero_warn('You are running on single node with no parallelization, so distributed has no effect.')
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self._distrib_type = None
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# for DDP overwrite nb processes by requested GPUs
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if (
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self._device_type == DeviceType.GPU
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and self._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN)
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):
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self.num_processes = self.num_gpus
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# Horovod is an extra case...
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if self.distributed_backend == "horovod":
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self._set_horovod_backend()
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# throw error to force user ddp or ddp2 choice
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_ddp = (DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2)
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if (self.num_nodes > 1 and self._distrib_type not in _ddp):
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raise MisconfigurationException(
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'DataParallel does not support num_nodes > 1. Switching to DistributedDataParallel for you. '
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'To silence this warning set `accelerator="ddp"` or `accelerator="ddp2"`'
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)
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rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self._device_type == DeviceType.GPU}')
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num_cores = self.tpu_cores if self.tpu_cores is not None else 0
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rank_zero_info(f'TPU available: {_TPU_AVAILABLE}, using: {num_cores} TPU cores')
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if torch.cuda.is_available() and self._device_type != DeviceType.GPU:
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rank_zero_warn("GPU available but not used. Set the --gpus flag when calling the script.")
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def _set_horovod_backend(self):
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self.check_horovod()
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self._distrib_type = DistributedType.HOROVOD
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# Initialize Horovod to get rank / size info
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hvd.init()
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if self.on_gpu:
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# Horovod assigns one local GPU per process
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self.parallel_device_ids = list(range(hvd.local_size()))
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self.root_gpu = hvd.local_rank()
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else:
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self.num_processes = hvd.local_size()
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def check_horovod(self):
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"""Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
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if not _HOROVOD_AVAILABLE:
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raise MisconfigurationException(
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'Requested `distributed_backend="horovod"`, but Horovod is not installed.'
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"Install with \n $HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]"
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)
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if self.num_gpus > 1 or self.num_nodes > 1:
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raise MisconfigurationException(
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"Horovod does not support setting num_nodes / num_gpus explicitly. Use "
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"horovodrun / mpirun to configure the number of processes."
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)
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@staticmethod
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def has_horovodrun():
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"""Returns True if running with `horovodrun` using Gloo or OpenMPI."""
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return "OMPI_COMM_WORLD_RANK" in os.environ or "HOROVOD_RANK" in os.environ
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def configure_slurm_ddp(self):
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# extract SLURM flag vars
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# whenever we have the correct number of tasks, we let slurm manage processes
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# otherwise we launch the required number of processes
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if self.use_ddp or self.use_ddp2:
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num_requested_gpus = self.num_gpus * self.num_nodes
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num_slurm_tasks = 0
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try:
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num_slurm_tasks = int(os.environ["SLURM_NTASKS"])
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self.is_slurm_managing_tasks = num_slurm_tasks == num_requested_gpus
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# enable slurm cpu
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if num_requested_gpus == 0:
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self.is_slurm_managing_tasks = num_slurm_tasks == self.num_processes
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# in interactive mode we don't manage tasks
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job_name = os.environ["SLURM_JOB_NAME"]
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if job_name == "bash":
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self.is_slurm_managing_tasks = False
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except Exception:
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# likely not on slurm, so set the slurm managed flag to false
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self.is_slurm_managing_tasks = False
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# used for tests only, set this flag to simulate slurm managing a task
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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.")
|