# 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 re import torch import torch.distributed as torch_distrib import torch.distributed as dist import torch.multiprocessing as mp from pytorch_lightning import _logger as log from pytorch_lightning.accelerators.base_backend import Accelerator from pytorch_lightning.utilities import AMPType from pytorch_lightning.utilities.cloud_io import atomic_save, load as pl_load from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn from pytorch_lightning.utilities.distributed import find_free_network_port from pytorch_lightning.distributed.dist import LightningDistributed try: from hydra.core.hydra_config import HydraConfig from hydra.utils import get_original_cwd, to_absolute_path except ImportError: HYDRA_AVAILABLE = False else: HYDRA_AVAILABLE = True class DDPCPUSpawnBackend(Accelerator): def __init__(self, trainer, nprocs, cluster_environment=None): super().__init__(trainer, cluster_environment) self.mp_queue = None self.nprocs = nprocs self.dist = LightningDistributed() def setup(self, model): os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port())) # pass in a state q smp = mp.get_context('spawn') self.mp_queue = smp.SimpleQueue() self.trainer.model = model def train(self): model = self.trainer.model # train in children process mp.spawn(self.ddp_train, nprocs=self.nprocs, args=(self.mp_queue, model,)) # restore main state with best weights best_path = self.mp_queue.get() results = self.mp_queue.get() # recover the weights of the processes trained in the children self.__recover_child_process_weights(model, best_path) return results def ddp_train(self, process_idx, mp_queue, model): """ Entry point for ddp Args: process_idx: mp_queue: multiprocessing queue model: Returns: """ # show progressbar only on progress_rank 0 if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None: self.trainer.progress_bar_callback.disable() # determine which process we are and world size self.set_world_ranks(process_idx) # 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}') 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 = model.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) # DDP spawn already spawned off each process... no need to do anything device_ids = self.get_device_ids() # allow user to configure ddp model = model.configure_ddp(model, device_ids) # set up training routine self.trainer.train_loop.setup_training(model) # train or test results = self.train_or_test() # get original model model = self.trainer.get_model() # persist info in ddp_spawn self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results) # clean up memory torch.cuda.empty_cache() 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 broadcast(self, obj, src=0): return self.dist.broadcast(obj) 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 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): model.cpu() def get_device_ids(self): device_ids = None return device_ids def __recover_child_process_weights(self, model, best_path): # transfer back the best path to the trainer if self.trainer.checkpoint_callback: self.trainer.checkpoint_callback.best_model_path = best_path self.trainer.model = model def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results): # track the best model path best_model_path = None if self.trainer.checkpoint_callback is not None: best_model_path = self.trainer.checkpoint_callback.best_model_path if self.trainer.global_rank == 0 and mp_queue is not None: rank_zero_warn('cleaning up ddp environment...') # todo, pass complete checkpoint as state dictionary mp_queue.put(best_model_path) mp_queue.put(results)