from pytorch_lightning import accelerators import os import torch from pytorch_lightning.utilities import device_parser from pytorch_lightning.utilities import rank_zero_only from pytorch_lightning.utilities.distributed import rank_zero_warn, rank_zero_info from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning import _logger as log try: import torch_xla except ImportError: XLA_AVAILABLE = False else: XLA_AVAILABLE = True try: import horovod.torch as hvd except (ModuleNotFoundError, ImportError): HOROVOD_AVAILABLE = False else: HOROVOD_AVAILABLE = True class AcceleratorConnector: def __init__(self, trainer): self.trainer = trainer def on_trainer_init( self, num_processes, tpu_cores, distributed_backend, auto_select_gpus, gpus, num_nodes, log_gpu_memory, sync_batchnorm, benchmark, replace_sampler_ddp, deterministic ): self.trainer.deterministic = deterministic torch.backends.cudnn.deterministic = self.trainer.deterministic if self.trainer.deterministic: # fixing non-deterministic part of horovod # https://github.com/PyTorchLightning/pytorch-lightning/pull/1572/files#r420279383 os.environ["HOROVOD_FUSION_THRESHOLD"] = str(0) # init the default rank if exists # we need to call this here or NVIDIA flags and other messaging in init will show on all ranks # this way we only show it on rank 0 if 'LOCAL_RANK' in os.environ: rank_zero_only.rank = int(os.environ['LOCAL_RANK']) # benchmarking self.trainer.benchmark = benchmark torch.backends.cudnn.benchmark = self.trainer.benchmark # Transfer params self.trainer.num_nodes = num_nodes self.trainer.log_gpu_memory = log_gpu_memory # sync-bn backend self.trainer.sync_batchnorm = sync_batchnorm self.trainer.tpu_cores = device_parser.parse_tpu_cores(tpu_cores) self.trainer.on_tpu = self.trainer.tpu_cores is not None self.trainer.tpu_id = self.trainer.tpu_cores[0] if isinstance(self.trainer.tpu_cores, list) else None if num_processes != 1 and distributed_backend != "ddp_cpu": rank_zero_warn("num_processes is only used for distributed_backend=\"ddp_cpu\". Ignoring it.") self.trainer.num_processes = num_processes # override with environment flag gpus = os.environ.get('PL_TRAINER_GPUS', gpus) # for gpus allow int, string and gpu list if auto_select_gpus and isinstance(gpus, int): self.trainer.gpus = self.trainer.tuner.pick_multiple_gpus(gpus) else: self.trainer.gpus = gpus self.trainer.data_parallel_device_ids = device_parser.parse_gpu_ids(self.trainer.gpus) self.trainer.root_gpu = device_parser.determine_root_gpu_device(self.trainer.data_parallel_device_ids) self.trainer.root_device = torch.device("cpu") self.trainer.on_gpu = True if (self.trainer.data_parallel_device_ids and torch.cuda.is_available()) else False # tpu state flags self.trainer.use_tpu = False self.trainer.tpu_local_core_rank = None self.trainer.tpu_global_core_rank = None # distributed backend choice self.trainer.distributed_backend = distributed_backend self.set_distributed_mode(distributed_backend) # override dist backend when using tpus if self.trainer.on_tpu: self.trainer.distributed_backend = 'tpu' self.trainer.use_tpu = True # init flags for SLURM+DDP to work self.trainer.world_size = 1 self.trainer.interactive_ddp_procs = [] # link up SLURM # TODO: this should be taken out of here... but depends too much on DDP self.trainer.slurm_connector.on_trainer_init(self.trainer.num_nodes) self.trainer.node_rank = self.determine_ddp_node_rank() self.trainer.local_rank = self.determine_local_rank() self.trainer.global_rank = 0 # NVIDIA setup self.set_nvidia_flags(self.trainer.is_slurm_managing_tasks, self.trainer.data_parallel_device_ids) self.trainer.on_colab_kaggle = os.getenv('COLAB_GPU') or os.getenv('KAGGLE_URL_BASE') self.trainer.replace_sampler_ddp = replace_sampler_ddp def select_accelerator(self): # SLURM ddp use_slurm_ddp = self.trainer.use_ddp and self.trainer.is_slurm_managing_tasks # torchelastic or general non_slurm ddp te_flags_passed = 'WORLD_SIZE' in os.environ and ('GROUP_RANK' in os.environ or 'NODE_RANK' in os.environ) use_torchelastic_ddp = self.trainer.use_ddp and te_flags_passed use_ddp_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_spawn' use_ddp_cpu_spawn = self.trainer.use_ddp and self.trainer.distributed_backend == 'ddp_cpu' # choose the appropriate accelerator backend if self.trainer.use_ddp2: accelerator_backend = accelerators.DDP2Backend(self.trainer) elif use_slurm_ddp: accelerator_backend = accelerators.DDPBackend(self.trainer, mode='slurm_ddp') elif use_torchelastic_ddp: accelerator_backend = accelerators.DDPBackend(self.trainer, mode='torchelastic_ddp') elif use_ddp_spawn: accelerator_backend = accelerators.DDPSpawnBackend(self.trainer, nprocs=self.trainer.num_processes) elif use_ddp_cpu_spawn: accelerator_backend = accelerators.DDPCPUSpawnBackend(self.trainer, nprocs=self.trainer.num_processes) elif self.trainer.distributed_backend == 'ddp': accelerator_backend = accelerators.DDPBackend(self.trainer, mode='ddp') elif self.trainer.use_dp: accelerator_backend = accelerators.DataParallelBackend(self.trainer) elif self.trainer.use_horovod: accelerator_backend = accelerators.HorovodBackend(self.trainer) elif self.trainer.use_single_gpu: accelerator_backend = accelerators.GPUBackend(self.trainer) elif self.trainer.use_tpu: accelerator_backend = accelerators.TPUBackend(self.trainer) else: accelerator_backend = accelerators.CPUBackend(self.trainer) return accelerator_backend def set_distributed_mode(self, distributed_backend): self.trainer.use_dp = False self.trainer.use_ddp = False self.trainer.use_ddp2 = False self.trainer.use_horovod = False self.trainer.use_single_gpu = False if distributed_backend is None: if self.has_horovodrun(): self._set_horovod_backend() elif self.trainer.num_gpus == 0: if self.trainer.num_nodes > 1 or self.trainer.num_processes > 1: self.trainer.use_ddp = True # ddp_cpu elif self.trainer.num_gpus == 1: self.trainer.use_single_gpu = True elif self.trainer.num_gpus > 1: rank_zero_warn( 'You requested multiple GPUs but did not specify a backend, e.g.' ' Trainer(distributed_backend=dp) (or ddp, ddp2).' ' Setting distributed_backend=ddp_spawn for you.' ) self.trainer.distributed_backend = 'ddp_spawn' distributed_backend = 'ddp_spawn' if distributed_backend == "dp": # do nothing if num_gpus == 0 if self.trainer.num_gpus == 1: self.trainer.use_single_gpu = True self.trainer.use_dp = True elif self.trainer.num_gpus > 1: self.trainer.use_dp = True elif distributed_backend in ['ddp', 'ddp_spawn']: if self.trainer.num_gpus == 0: if self.trainer.num_nodes > 1 or self.trainer.num_processes > 1: self.trainer.use_ddp = True # ddp_cpu elif self.trainer.num_gpus == 1: self.trainer.use_single_gpu = True self.trainer.use_ddp = True elif self.trainer.num_gpus > 1: self.trainer.use_ddp = True self.trainer.num_processes = self.trainer.num_gpus elif distributed_backend == "ddp2": # do nothing if num_gpus == 0 if self.trainer.num_gpus >= 1: self.trainer.use_ddp2 = True elif distributed_backend == "ddp_cpu": if self.trainer.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.trainer.use_ddp = True self.trainer.data_parallel_device_ids = None self.trainer.on_gpu = False elif distributed_backend == 'horovod': self._set_horovod_backend() # throw error to force user ddp or ddp2 choice if self.trainer.num_nodes > 1 and not (self.trainer.use_ddp2 or self.trainer.use_ddp): raise MisconfigurationException( 'DataParallel does not support num_nodes > 1. Switching to DistributedDataParallel for you. ' 'To silence this warning set distributed_backend=ddp or distributed_backend=ddp2' ) rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self.trainer.on_gpu}') num_cores = self.trainer.tpu_cores if self.trainer.tpu_cores is not None else 0 rank_zero_info(f'TPU available: {XLA_AVAILABLE}, using: {num_cores} TPU cores') if torch.cuda.is_available() and not self.trainer.on_gpu: rank_zero_warn('GPU available but not used. Set the --gpus flag when calling the script.') def _set_horovod_backend(self): self.check_horovod() self.trainer.use_horovod = True # Initialize Horovod to get rank / size info hvd.init() if self.trainer.on_gpu: # Horovod assigns one local GPU per process self.trainer.root_gpu = hvd.local_rank() 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.trainer.num_gpus > 1 or self.trainer.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(): """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 set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids): if data_parallel_device_ids is None: return # set the correct cuda visible devices (using pci order) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # when slurm is managing the task it sets the visible devices if not is_slurm_managing_tasks and 'CUDA_VISIBLE_DEVICES' not in os.environ: if isinstance(data_parallel_device_ids, int): id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids))) os.environ["CUDA_VISIBLE_DEVICES"] = id_str else: gpu_str = ','.join([str(x) for x in data_parallel_device_ids]) os.environ["CUDA_VISIBLE_DEVICES"] = gpu_str # don't make this debug... this is good UX rank_zero_info(f'CUDA_VISIBLE_DEVICES: [{os.environ["CUDA_VISIBLE_DEVICES"]}]') def determine_local_rank(self): if self.trainer.is_slurm_managing_tasks: return int(os.environ['SLURM_LOCALID']) else: return int(os.environ.get('LOCAL_RANK', 0)) def determine_ddp_node_rank(self): if self.trainer.is_slurm_managing_tasks: return int(os.environ['SLURM_NODEID']) # torchelastic uses the envvar GROUP_RANK, whereas other systems(?) use NODE_RANK. # otherwise use given node rank or default to node rank 0 env_vars = ['NODE_RANK', 'GROUP_RANK'] node_ids = [(k, os.environ.get(k, None)) for k in env_vars] node_ids = [(k, v) for k, v in node_ids if v is not None] if len(node_ids) == 0: return 0 if len(node_ids) > 1: log.warning(f"Multiple environment variables ({node_ids}) defined for node rank. Using the first one.") k, rank = node_ids.pop() rank_zero_info(f"Using environment variable {k} for node rank ({rank}).") return int(rank)