# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License import os import torch import torch.distributed as torch_distrib import torch.distributed as dist from pytorch_lightning.accelerators.base_accelerator import Accelerator from pytorch_lightning import _logger as log from pytorch_lightning.utilities import AMPType from pytorch_lightning.utilities.distributed import rank_zero_only from pytorch_lightning.utilities.seed import seed_everything from pytorch_lightning.distributed.dist import LightningDistributed from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel from torch.nn.parallel import DistributedDataParallel from typing import List try: from hydra.utils import to_absolute_path, get_original_cwd from hydra.core.hydra_config import HydraConfig except ImportError: HYDRA_AVAILABLE = False else: HYDRA_AVAILABLE = True # ------------------------------------------- # !!!!!!!!!!!!!! NOTE !!!!!!!!!!!!!!!!!!!!!! # TEMP CLASS WHILE WE DECOUPLE SLURM FROM DDP # !!!!!!!!!!!!!! NOTE !!!!!!!!!!!!!!!!!!!!!! # ------------------------------------------- class DDPSLURMBackend(Accelerator): def __init__(self, trainer, cluster_environment=None): super().__init__(trainer, cluster_environment) self.task_idx = None self._has_spawned_children = False self.dist = LightningDistributed() def setup(self, model): self.trainer.model = model self.task_idx = int(os.environ['SLURM_LOCALID']) def train(self): model = self.trainer.model self.ddp_train(process_idx=self.task_idx, model=model) def set_world_ranks(self, process_idx): self.trainer.local_rank = process_idx self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes def model_to_device(self, model, process_idx): self.trainer.root_gpu = process_idx torch.cuda.set_device(self.trainer.root_gpu) model.cuda(self.trainer.root_gpu) def get_device_ids(self): device_ids = [self.trainer.root_gpu] return device_ids def training_step(self, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = self.trainer.model(*args) else: output = self.trainer.model(*args) return output def validation_step(self, args): output = self.training_step(args) return output def test_step(self, args): output = self.training_step(args) return output def barrier(self, name: str = None): if torch_distrib.is_initialized(): torch_distrib.barrier() def early_stopping_should_stop(self, pl_module): stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device) dist.all_reduce(stop, op=dist.reduce_op.SUM) dist.barrier() should_stop = stop == self.trainer.world_size return should_stop def broadcast(self, obj, src=0): return self.dist.broadcast(obj) def ddp_train(self, process_idx, model): """ Entry point for ddp Args: process_idx: mp_queue: multiprocessing queue model: Returns: """ seed = os.environ.get("PL_GLOBAL_SEED") if seed is not None: seed_everything(int(seed)) # determine which process we are and world size self.set_world_ranks(process_idx) # toggle prog bar if self.trainer.global_rank == 0 and self.trainer.progress_bar_callback is not None: self.trainer.progress_bar_callback.disable() # set warning rank rank_zero_only.rank = self.trainer.global_rank # set up server using proc 0's ip address # try to init for 20 times at max in case ports are taken # where to store ip_table model.trainer = self.trainer self.init_ddp_connection( self.trainer.global_rank, self.trainer.world_size, self.trainer.is_slurm_managing_tasks ) # call setup after the ddp process has connected self.trainer.call_setup_hook(model) # on world_size=0 let everyone know training is starting if self.trainer.is_global_zero and not torch.distributed.is_initialized(): log.info('-' * 100) log.info(f'distributed_backend={self.trainer.distributed_backend} (on SLURM)') log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes') log.info('-' * 100) # call sync_bn before .cuda(), configure_apex and configure_ddp if self.trainer.sync_batchnorm: model = self.configure_sync_batchnorm(model) # move the model to the correct device self.model_to_device(model, process_idx) # CHOOSE OPTIMIZER # allow for lr schedulers as well self.setup_optimizers(model) # set model properties before going into wrapper self.trainer.model_connector.copy_trainer_model_properties(model) # 16-bit model = self.trainer.precision_connector.connect(model) # device ids change depending on the DDP setup device_ids = self.get_device_ids() # allow user to configure ddp model = self.configure_ddp(model, device_ids) # set up training routine self.trainer.train_loop.setup_training(model) # train or test results = self.train_or_test() # clean up memory torch.cuda.empty_cache() return results def configure_ddp( self, model: "LightningModule", device_ids: List[int] ) -> DistributedDataParallel: model = LightningDistributedDataParallel( model, device_ids=device_ids, find_unused_parameters=True ) return model def configure_sync_batchnorm(self, model: "LightningModule") -> "LightningModule": """ Add global batchnorm for a model spread across multiple GPUs and nodes. Override to synchronize batchnorm between specific process groups instead of the whole world or use a different sync_bn like `apex`'s version. Args: model: pointer to current :class:`LightningModule`. Return: LightningModule with batchnorm layers synchronized between process groups """ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None) return model