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Source code for pytorch_lightning.accelerators.ddp_spawn_accelerator

# 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
from typing import Any, List, Optional, Union

import torch
import torch.multiprocessing as mp
import torch.distributed as torch_distrib
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel

from pytorch_lightning import _logger as log
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.core.lightning import LightningModule
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, find_free_network_port
from pytorch_lightning.utilities.distributed import sync_ddp_if_available
from pytorch_lightning.utilities.seed import seed_everything
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


[docs]class DDPSpawnAccelerator(Accelerator): def __init__(self, trainer, nprocs, cluster_environment=None, ddp_plugin=None): """ Runs training using DDP using mp.spawn via manual launch (not cluster launch) Example:: # default trainer = Trainer(accelerator=DDPSpawnAccelerator()) """ super().__init__(trainer, cluster_environment, ddp_plugin) self.mp_queue = None self.nprocs = nprocs self.dist = LightningDistributed() self.nickname = 'ddp' 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() last_path = self.mp_queue.get() # recover the weights of the processes trained in the children self.__recover_child_process_weights(model, best_path, last_path) return results
[docs] def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0): """ Entry point for ddp Args: process_idx: mp_queue: multiprocessing queue model: """ 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 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 = self.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 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() # 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 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, is_master): gpu_idx = self.trainer.data_parallel_device_ids[self.trainer.local_rank] self.trainer.root_gpu = gpu_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: Optional[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 __recover_child_process_weights(self, model, best_path, last_path): # transfer back the best path to the trainer if self.trainer.checkpoint_callback: self.trainer.checkpoint_callback.best_model_path = best_path # todo, pass also best score # load last weights if last_path is not None and not self.trainer.testing: ckpt = pl_load(last_path, map_location=lambda storage, loc: storage) model.load_state_dict(ckpt) self.trainer.model = model def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results): 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 configure_ddp( self, model: LightningModule, device_ids: List[int] ) -> DistributedDataParallel: model = self.ddp_plugin.configure_ddp(model, device_ids) return model
[docs] 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
[docs] def sync_tensor(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor: return sync_ddp_if_available(tensor, group, reduce_op)

© Copyright Copyright (c) 2018-2020, William Falcon et al... Revision 5df40a4a.

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