258 lines
9.1 KiB
Python
258 lines
9.1 KiB
Python
# 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
|
|
from typing import Any, List, Optional, Union
|
|
|
|
import torch
|
|
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.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
|
|
from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, rank_zero_only, sync_ddp_if_available
|
|
|
|
|
|
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)
|
|
|
|
def ddp_train(self, process_idx, model):
|
|
"""
|
|
Entry point for ddp
|
|
|
|
Args:
|
|
process_idx:
|
|
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)
|
|
|
|
# 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)
|
|
|
|
self.trainer.convert_to_lightning_optimizers()
|
|
|
|
# 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()
|
|
|
|
# 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
|
|
|
|
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
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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
|