330 lines
12 KiB
Python
330 lines
12 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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 re
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from typing import Any, List, Optional, Union
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import torch
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import torch.distributed as torch_distrib
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import torch.multiprocessing as mp
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from torch.nn.parallel import DistributedDataParallel
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from pytorch_lightning import _logger as log
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from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
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from pytorch_lightning.cluster_environments import ClusterEnvironment
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.distributed import LightningDistributed
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from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
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from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.cloud_io import atomic_save
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from pytorch_lightning.utilities.cloud_io import load as pl_load
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from pytorch_lightning.utilities.distributed import (
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all_gather_ddp_if_available,
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find_free_network_port,
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rank_zero_only,
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rank_zero_warn,
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sync_ddp_if_available,
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)
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from pytorch_lightning.utilities.seed import seed_everything
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class DDPSpawnAccelerator(Accelerator):
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def __init__(self,
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trainer,
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nprocs: int,
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cluster_environment: Optional[ClusterEnvironment] = None,
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ddp_plugin: Optional[DDPPlugin] = None):
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"""
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Runs training using DDP using mp.spawn via manual launch (not cluster launch)
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Example::
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# default
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trainer = Trainer(accelerator=DDPSpawnAccelerator())
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"""
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super().__init__(trainer, cluster_environment, ddp_plugin)
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self.mp_queue = None
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self.nprocs = nprocs
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self.dist = LightningDistributed()
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self.nickname = 'ddp'
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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, 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 ddp_train(self, process_idx, mp_queue, model, is_master: bool = False, proc_offset: int = 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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"""
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seed = os.environ.get("PL_GLOBAL_SEED")
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if seed is not None:
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seed_everything(int(seed))
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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.set_world_ranks(process_idx)
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# set warning rank
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rank_zero_only.rank = self.trainer.global_rank
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# Initialize cuda device
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self.init_device(process_idx, is_master)
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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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self.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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if isinstance(self.ddp_plugin, RPCPlugin):
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if not self.ddp_plugin.is_main_rpc_process:
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self.ddp_plugin.on_accelerator_exit_rpc_process(self.trainer)
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self.ddp_plugin.exit_rpc_process()
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if self.ddp_plugin.return_after_exit_rpc_process:
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return
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else:
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self.ddp_plugin.on_main_rpc_connection(self.trainer)
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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 and not torch.distributed.is_initialized():
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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 = self.configure_sync_batchnorm(model)
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# move the model to the correct device
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self.model_to_device(model)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.setup_optimizers(model)
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self.ddp_plugin.on_after_setup_optimizers(self.trainer)
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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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# 16-bit
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model = self.trainer.precision_connector.connect(model)
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self.trainer.convert_to_lightning_optimizers()
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# device ids change depending on the DDP setup
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device_ids = self.get_device_ids()
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# allow user to configure ddp
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model = self.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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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 init_device(self, process_idx, is_master):
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# Todo: required argument `process_idx` is not used
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# Todo: required argument `is_master` is not used
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gpu_idx = self.trainer.data_parallel_device_ids[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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def model_to_device(self, model):
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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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def training_step(self, args):
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return self._step(args)
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def validation_step(self, args):
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return self._step(args)
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def test_step(self, args):
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return self._step(args)
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def _step(self, args):
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args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
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if self.trainer.amp_backend == AMPType.NATIVE:
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with torch.cuda.amp.autocast():
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output = self.trainer.model(*args)
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else:
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output = self.trainer.model(*args)
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return output
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def barrier(self, name: Optional[str] = None):
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if torch_distrib.is_initialized():
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torch_distrib.barrier()
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def early_stopping_should_stop(self, pl_module):
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stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
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torch_distrib.all_reduce(stop, op=torch_distrib.reduce_op.SUM)
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torch_distrib.barrier()
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should_stop = stop == self.trainer.world_size
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return should_stop
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def broadcast(self, obj, src=0):
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return self.dist.broadcast(obj)
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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 = pl_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 transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
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best_model_path = None
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if self.trainer.checkpoint_callback is not None:
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best_model_path = self.trainer.checkpoint_callback.best_model_path
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if self.trainer.global_rank == 0 and mp_queue is not None:
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rank_zero_warn('cleaning up ddp environment...')
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# todo, pass complete checkpoint as state dictionary
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mp_queue.put(best_model_path)
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mp_queue.put(results)
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# save the last weights
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last_path = None
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if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
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last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
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atomic_save(model.state_dict(), last_path)
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mp_queue.put(last_path)
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def configure_ddp(
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self, model: LightningModule, device_ids: List[int]
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) -> DistributedDataParallel:
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model = self.ddp_plugin.configure_ddp(model, device_ids)
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return model
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def configure_sync_batchnorm(self, model: LightningModule) -> LightningModule:
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"""
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Add global batchnorm for a model spread across multiple GPUs and nodes.
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Override to synchronize batchnorm between specific process groups instead
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of the whole world or use a different sync_bn like `apex`'s version.
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Args:
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model: pointer to current :class:`LightningModule`.
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Return:
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LightningModule with batchnorm layers synchronized between process groups
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"""
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)
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return model
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def sync_tensor(self,
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tensor: Union[torch.Tensor],
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group: Optional[Any] = None,
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reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
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return sync_ddp_if_available(tensor, group, reduce_op)
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def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
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"""
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Function to gather a tensor from several distributed processes
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Args:
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tensor: tensor of shape (batch, ...)
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group: the process group to gather results from. Defaults to all processes (world)
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sync_grads: flag that allows users to synchronize gradients for all_gather op
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Return:
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A tensor of shape (world_size, batch, ...)
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"""
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return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
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def get_reference_model(self, model) -> LightningModule:
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return self.ddp_plugin.get_model_from_plugin(model)
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@property
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def distributed_sampler_kwargs(self):
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distributed_sampler_kwargs = dict(
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num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
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rank=self.trainer.global_rank
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)
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if self.ddp_plugin is not None:
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distributed_sampler_kwargs = self.ddp_plugin.distributed_sampler_kwargs(distributed_sampler_kwargs)
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return distributed_sampler_kwargs
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@property
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def require_distributed_sampler(self):
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return True
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