ref: merge backends x/n (#3482)
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William Falcon
GitHub
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@ -133,124 +133,11 @@ class DDPBackend(DDPBase):
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def train(self):
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model = self.trainer.model
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if self.mode == 'ddp':
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results = self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
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results = self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
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del os.environ['WORLD_SIZE']
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return results
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else:
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self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
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def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
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"""
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Entry point for ddp
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Args:
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process_idx:
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mp_queue: multiprocessing queue
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model:
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is_master:
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proc_offset:
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Returns:
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"""
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# offset the process id if requested
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process_idx = process_idx + proc_offset
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# show progressbar only on progress_rank 0
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if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
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self.trainer.progress_bar_callback.disable()
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# determine which process we are and world size
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self.trainer.local_rank = process_idx
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self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
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self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
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# set warning rank
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rank_zero_only.rank = self.trainer.global_rank
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# set up server using proc 0's ip address
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# try to init for 20 times at max in case ports are taken
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# where to store ip_table
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model.trainer = self.trainer
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model.init_ddp_connection(
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self.trainer.global_rank,
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self.trainer.world_size,
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self.trainer.is_slurm_managing_tasks
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)
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# call setup after the ddp process has connected
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self.trainer.call_setup_hook(model)
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# on world_size=0 let everyone know training is starting
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if self.trainer.is_global_zero:
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log.info('-' * 100)
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log.info(f'distributed_backend={self.trainer.distributed_backend}')
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log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
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log.info('-' * 100)
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# call sync_bn before .cuda(), configure_apex and configure_ddp
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if self.trainer.sync_batchnorm:
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model = model.configure_sync_batchnorm(model)
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# MODEL
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# copy model to each gpu
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if self.trainer.on_gpu:
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gpu_idx = process_idx
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# when using ddp, the master process (proc 0) continues running as the main one
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# this means that the local rank will always be 0
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# (even if cuda visible devices has other visible gpus)
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# this means that the master process needs to pull the 0th visible index as the device number
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if is_master:
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available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
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gpu_idx = int(available_gpus[self.trainer.local_rank])
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self.trainer.root_gpu = gpu_idx
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torch.cuda.set_device(self.trainer.root_gpu)
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model.cuda(self.trainer.root_gpu)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
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self.trainer.optimizers = optimizers
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self.trainer.lr_schedulers = lr_schedulers
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self.trainer.optimizer_frequencies = optimizer_frequencies
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# set model properties before going into wrapper
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# AMP - run through amp wrapper before going to distributed DP
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if self.trainer.amp_backend == AMPType.APEX:
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model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
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self.trainer.optimizers = optimizers
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self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
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# DDP2 uses all GPUs on the machine
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if self.trainer.distributed_backend == 'ddp' or self.trainer.distributed_backend == 'ddp_spawn':
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device_ids = [self.trainer.root_gpu]
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else: # includes ddp_cpu
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device_ids = None
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# allow user to configure ddp
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model = model.configure_ddp(model, device_ids)
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# set up training routine
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self.trainer.train_loop.setup_training(model)
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# train or test
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results = self.train_or_test()
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# get original model
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model = self.trainer.get_model()
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# persist info in ddp_spawn
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self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
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# clean up memory
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torch.cuda.empty_cache()
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if self.trainer.global_rank == 0 and self.trainer.distributed_backend not in ['ddp_spawn', 'ddp_cpu']:
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return results
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self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
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def _check_can_spawn_children(self):
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if self._has_spawned_children:
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@ -258,3 +145,27 @@ class DDPBackend(DDPBase):
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"You tried to run `.fit` or `.test` multiple times in the same script."
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" This is not supported in DDP mode, switch to `distributed_backend='ddp_spawn'` instead."
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)
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def set_world_ranks(self, process_idx):
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self.trainer.local_rank = process_idx
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self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
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self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
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def model_to_device(self, model, process_idx, is_master):
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gpu_idx = process_idx
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# when using ddp, the master process (proc 0) continues running as the main one
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# this means that the local rank will always be 0
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# (even if cuda visible devices has other visible gpus)
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# this means that the master process needs to pull the 0th visible index as the device number
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if is_master:
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available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
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gpu_idx = int(available_gpus[self.trainer.local_rank])
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self.trainer.root_gpu = gpu_idx
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torch.cuda.set_device(self.trainer.root_gpu)
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model.cuda(self.trainer.root_gpu)
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def get_device_ids(self):
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device_ids = [self.trainer.root_gpu]
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return device_ids
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@ -180,6 +180,9 @@ class DDPBase(Accelerator):
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# clean up memory
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torch.cuda.empty_cache()
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if self.trainer.global_rank == 0:
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return results
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def set_world_ranks(self, process_idx):
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raise NotImplementedError('to create a ddp backend, please implement set_world_ranks')
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@ -11,68 +11,10 @@
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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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import torch.multiprocessing as mp
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from pytorch_lightning.utilities.distributed import find_free_network_port
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from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
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try:
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from apex import amp
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except ImportError:
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amp = None
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from pytorch_lightning.accelerators.ddp_spawn_backend import DDPSpawnBackend
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class DDPCPUSpawnBackend(DDPBase):
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def __init__(self, trainer, nprocs):
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super().__init__(trainer)
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self.mp_queue = None
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self.nprocs = nprocs
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def setup(self, model):
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os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
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# pass in a state q
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smp = mp.get_context('spawn')
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self.mp_queue = smp.SimpleQueue()
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self.trainer.model = model
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def train(self):
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model = self.trainer.model
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# train in children process
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mp.spawn(self.ddp_train_tmp, nprocs=self.nprocs, args=(self.mp_queue, model,))
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# restore main state with best weights
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best_path = self.mp_queue.get()
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results = self.mp_queue.get()
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last_path = self.mp_queue.get()
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# recover the weights of the processes trained in the children
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self.__recover_child_process_weights(model, best_path, last_path)
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return results
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def __recover_child_process_weights(self, model, best_path, last_path):
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# transfer back the best path to the trainer
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if self.trainer.checkpoint_callback:
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self.trainer.checkpoint_callback.best_model_path = best_path
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# todo, pass also best score
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# load last weights
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if last_path is not None and not self.trainer.testing:
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ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
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model.load_state_dict(ckpt)
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self.trainer.model = model
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def set_world_ranks(self, process_idx):
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self.trainer.local_rank = process_idx
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self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
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self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
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class DDPCPUSpawnBackend(DDPSpawnBackend):
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def model_to_device(self, model, process_idx, is_master):
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pass
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