# 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 from pytorch_lightning.utilities import AMPType from pytorch_lightning.accelerators.base_backend import Accelerator import torch.distributed as torch_distrib import torch.distributed as dist from pytorch_lightning.utilities.cloud_io import atomic_save from pytorch_lightning.utilities.distributed import rank_zero_warn, rank_zero_only from pytorch_lightning import _logger as log from pytorch_lightning.utilities.seed import seed_everything 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 class DDPBase(Accelerator): def __init__(self, trainer): super().__init__(trainer) 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): 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 transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results): if self.trainer.distributed_backend.lower() not in ['ddp_spawn', 'ddp_cpu', 'tpu']: return # 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) # save the last weights last_path = None if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0: last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path) atomic_save(model.state_dict(), last_path) mp_queue.put(last_path) def ddp_train_tmp(self, process_idx, mp_queue, model, is_master=False, proc_offset=0): """ 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)) # offset the process id if requested process_idx = process_idx + proc_offset # 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 model.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: 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, is_master) # CHOOSE OPTIMIZER # allow for lr schedulers as well optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model) self.trainer.optimizers = optimizers self.trainer.lr_schedulers = lr_schedulers self.trainer.optimizer_frequencies = optimizer_frequencies # set model properties before going into wrapper self.trainer.model_connector.copy_trainer_model_properties(model) # AMP - # run through amp wrapper before going to distributed DP model, optimizers = self.trainer.precision_connector.connect(model, optimizers) # device ids change depending on the DDP setup 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() if self.trainer.global_rank == 0: return results def set_world_ranks(self, process_idx): raise NotImplementedError('to create a ddp backend, please implement set_world_ranks') def model_to_device(self, model, process_idx, is_master): raise NotImplementedError('to create a ddp backend, please implement model_to_device') def get_device_ids(self): raise NotImplementedError('to create a ddp backend, please implement get_device_ids')