Source code for pytorch_lightning.accelerators.ddp_hpc_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 logging
import os
from typing import Any, List, Optional, Union
import torch
import torch.distributed as dist
import torch.distributed as torch_distrib
from torch.nn.parallel import DistributedDataParallel
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.distributed.dist import LightningDistributed
from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, rank_zero_only, sync_ddp_if_available
log = logging.getLogger(__name__)
if HYDRA_AVAILABLE:
from hydra.core.hydra_config import HydraConfig
from hydra.utils import get_original_cwd, to_absolute_path
[docs]class DDPHPCAccelerator(Accelerator):
def __init__(self,
trainer,
cluster_environment: Optional[ClusterEnvironment] = None,
ddp_plugin: Optional[DDPPlugin] = None):
"""
Runs training using DDP on an HPC cluster
Example::
# default
trainer = Trainer(accelerator=DDPHPCAccelerator())
"""
super().__init__(trainer, cluster_environment, ddp_plugin)
self.task_idx = None
self._has_spawned_children = False
self.dist = LightningDistributed()
self.nickname = 'ddp'
def setup(self, model):
self.trainer.model = model
self.task_idx = self.cluster_environment.local_rank()
def train(self):
model = self.trainer.model
self.ddp_train(process_idx=self.task_idx, model=model)
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 init_device(self, process_idx):
self.trainer.root_gpu = process_idx
torch.cuda.set_device(self.trainer.root_gpu)
def model_to_device(self, model):
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):
return self._step(args)
def validation_step(self, args):
return self._step(args)
def test_step(self, args):
return self._step(args)
def _step(self, args):
args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *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 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)
[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
"""
# determine which process we are and world size
self.set_world_ranks(process_idx)
self.init_device(process_idx)
# toggle prog bar
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()
# 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
)
if isinstance(self.ddp_plugin, RPCPlugin):
if not self.ddp_plugin.is_main_rpc_process:
self.ddp_plugin.on_accelerator_exit_rpc_process(self.trainer)
self.ddp_plugin.exit_rpc_process()
if self.ddp_plugin.return_after_exit_rpc_process:
return
else:
self.ddp_plugin.on_main_rpc_connection(self.trainer)
# 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)
# CHOOSE OPTIMIZER
# allow for lr schedulers as well
self.setup_optimizers(model)
self.ddp_plugin.on_after_setup_optimizers(self.trainer)
# 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)
self.trainer.setup_trainer(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:
self.ddp_plugin.device_ids = device_ids
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)
[docs] def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
"""
Function to gather a tensor from several distributed processes
Args:
tensor: tensor of shape (batch, ...)
group: the process group to gather results from. Defaults to all processes (world)
sync_grads: flag that allows users to synchronize gradients for all_gather op
Return:
A tensor of shape (world_size, batch, ...)
"""
return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
[docs] def get_reference_model(self, model) -> LightningModule:
return self.ddp_plugin.get_model_from_plugin(model)
@property
def distributed_sampler_kwargs(self):
distributed_sampler_kwargs = dict(
num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
rank=self.trainer.global_rank
)
if self.ddp_plugin is not None:
distributed_sampler_kwargs = self.ddp_plugin.distributed_sampler_kwargs(distributed_sampler_kwargs)
return distributed_sampler_kwargs
@property
def require_distributed_sampler(self):
return True
