Source code for pytorch_lightning.accelerators.ddp_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 torch
import torch.distributed as torch_distrib
import subprocess
import sys
from os.path import abspath
from time import sleep
from typing import Any, Optional, List, Union
import numpy as np
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.distributed.dist import LightningDistributed
from pytorch_lightning.utilities import AMPType
from pytorch_lightning.utilities.distributed import find_free_network_port
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities.distributed import sync_ddp_if_available
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.seed import seed_everything
from torch.nn.parallel import DistributedDataParallel
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
[docs]class DDPAccelerator(Accelerator):
def __init__(self, trainer, cluster_environment=None, ddp_plugin=None):
"""
Runs training using DDP strategy on a single machine (manually, not via cluster start)
Example::
# default
trainer = Trainer(accelerator=DDPAccelerator())
"""
super().__init__(trainer, cluster_environment, ddp_plugin)
self.task_idx = None
self._has_spawned_children = False
self.interactive_ddp_procs = []
self.dist = LightningDistributed()
self.nickname = 'ddp'
def setup(self, model):
# first track model
self.trainer.model = model
# start the other scripts
if os.environ.get('PL_IN_DDP_SUBPROCESS', '0') != '1':
self._call_children_scripts()
# set the task idx
self.task_idx = int(os.environ['LOCAL_RANK'])
def _call_children_scripts(self):
assert self.trainer.global_rank == 0
self._check_can_spawn_children()
self._has_spawned_children = True
os.environ['MASTER_ADDR'] = os.environ.get('MASTER_ADDR', '127.0.0.1')
os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
# allow the user to pass the node rank
node_rank = '0'
node_rank = os.environ.get('NODE_RANK', node_rank)
node_rank = os.environ.get('GROUP_RANK', node_rank)
os.environ['NODE_RANK'] = node_rank
os.environ['LOCAL_RANK'] = '0'
# when user is using hydra find the absolute path
path_lib = abspath if not HYDRA_AVAILABLE else to_absolute_path
# pull out the commands used to run the script and resolve the abs file path
command = sys.argv
try:
full_path = path_lib(command[0])
except Exception as e:
full_path = abspath(command[0])
command[0] = full_path
# use the same python interpreter and actually running
command = [sys.executable] + command
# the visible devices tell us how many GPUs we want to use.
# when the trainer script was called the device has already been scoped by the time
# code reaches this point. so, to call the scripts, we need to leave cuda visible devices alone
# but forward the GPUs selected via environment variables
if self.trainer.data_parallel_device_ids is None:
raise MisconfigurationException('you selected (distribute_backend = ddp) but did not set Trainer(gpus=?)')
os.environ['PL_TRAINER_GPUS'] = ','.join([str(i) for i in self.trainer.data_parallel_device_ids])
os.environ['PL_IN_DDP_SUBPROCESS'] = '1'
if self.trainer.logger is not None:
os.environ['PL_EXP_VERSION'] = str(self.trainer.logger.version)
num_gpus = len(self.trainer.data_parallel_device_ids)
os.environ['WORLD_SIZE'] = f'{num_gpus * self.trainer.num_nodes}'
self.interactive_ddp_procs = []
for local_rank in range(1, self.trainer.num_processes):
env_copy = os.environ.copy()
env_copy['LOCAL_RANK'] = f'{local_rank}'
# remove env var if global seed not set
if os.environ.get('PL_GLOBAL_SEED') is None and 'PL_GLOBAL_SEED' in env_copy:
del env_copy['PL_GLOBAL_SEED']
# start process
# if hydra is available and initialized, make sure to set the cwd correctly
cwd: Optional[str] = None
if HYDRA_AVAILABLE:
if HydraConfig.initialized():
cwd = get_original_cwd()
proc = subprocess.Popen(command, env=env_copy, cwd=cwd)
self.interactive_ddp_procs.append(proc)
# starting all processes at once can cause issues
# with dataloaders delay between 1-10 seconds
delay = np.random.uniform(1, 5, 1)[0]
sleep(delay)
def train(self):
model = self.trainer.model
results = self.ddp_train(process_idx=self.task_idx, model=model)
if 'WORLD_SIZE' in os.environ:
del os.environ['WORLD_SIZE']
return results
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 _check_can_spawn_children(self):
if self._has_spawned_children:
raise RuntimeError(
"You tried to run `.fit` or `.test` multiple times in the same script."
" This is not supported in DDP mode, switch to `distributed_backend='ddp_spawn'` instead."
)
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):
self.trainer.root_gpu = self.trainer.data_parallel_device_ids[self.trainer.local_rank]
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 on_train_end(self):
pass
def early_stopping_should_stop(self, pl_module):
stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
torch_distrib.all_reduce(stop, op=torch_distrib.reduce_op.SUM)
torch_distrib.barrier()
should_stop = stop == self.trainer.world_size
return should_stop
def broadcast(self, obj, src=0):
return self.dist.broadcast(obj)
[docs] def ddp_train(self, process_idx, model):
"""
Entry point for ddp
Args:
process_idx:
mp_queue: multiprocessing queue
model:
Returns:
Dict with evaluation results
"""
seed = os.environ.get("PL_GLOBAL_SEED")
if seed is not None:
seed_everything(int(seed))
# 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)
# 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.barrier('ddp_setup')
self.trainer.train_loop.setup_training(model)
# train or test
results = self.train_or_test()
# clean up memory
torch.cuda.empty_cache()
return results
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)
