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 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)
