Source code for pytorch_lightning.plugins.training_type.ddp
# 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
import shutil
import signal
import subprocess
import sys
import tempfile
import time
from pathlib import Path
from time import sleep
from typing import Any, Dict, List, Optional, Union
import __main__
import numpy as np
import torch
import torch.distributed
from torch.nn.parallel.distributed import DistributedDataParallel
from pytorch_lightning.distributed import LightningDistributed
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.overrides.distributed import prepare_for_backward
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
from pytorch_lightning.utilities import (
_HYDRA_AVAILABLE,
_TORCH_GREATER_EQUAL_1_7,
_TORCH_GREATER_EQUAL_1_8,
_TORCH_GREATER_EQUAL_1_9,
rank_zero_deprecation,
rank_zero_warn,
)
from pytorch_lightning.utilities.distributed import (
distributed_available,
rank_zero_info,
rank_zero_only,
ReduceOp,
sync_ddp_if_available,
)
from pytorch_lightning.utilities.exceptions import DeadlockDetectedException, MisconfigurationException
from pytorch_lightning.utilities.seed import reset_seed
if _HYDRA_AVAILABLE:
from hydra.core.hydra_config import HydraConfig
from hydra.utils import get_original_cwd, to_absolute_path
if _TORCH_GREATER_EQUAL_1_8:
from pytorch_lightning.utilities.distributed import register_ddp_comm_hook
log = logging.getLogger(__name__)
[docs]class DDPPlugin(ParallelPlugin):
"""
Plugin for multi-process single-device training on one or multiple nodes.
The master process in each node spawns N-1 child processes via :func:`subprocess.Popen`,
where N is the number of devices (e.g. GPU) per node.
It is very similar to how :mod:`torch.distributed.launch` launches processes.
"""
distributed_backend = "ddp"
def __init__(
self,
parallel_devices: Optional[List[torch.device]] = None,
num_nodes: Optional[int] = None,
cluster_environment: ClusterEnvironment = None,
sync_batchnorm: Optional[bool] = None,
ddp_comm_state: Optional[object] = None,
ddp_comm_hook: Optional[callable] = None,
ddp_comm_wrapper: Optional[callable] = None,
**kwargs: Union[Any, Dict[str, Any]],
) -> None:
super().__init__(parallel_devices=parallel_devices, cluster_environment=cluster_environment)
self.interactive_ddp_procs = []
if num_nodes is not None:
rank_zero_deprecation(
"Argument `num_nodes` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6."
" Notice that it will be overriden by the trainer setting."
)
self._num_nodes = num_nodes or 1
if sync_batchnorm is not None:
rank_zero_deprecation(
"Argument `sync_batchnorm` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6."
" Notice that it will be overriden by the trainer setting."
)
self._sync_batchnorm = sync_batchnorm or False
self.dist = LightningDistributed()
self.num_processes = len(self.parallel_devices) if self.parallel_devices is not None else 0
self._ddp_kwargs = kwargs
self._has_spawned_children = False
self._task_idx = None
self._ddp_comm_state = ddp_comm_state
self._ddp_comm_hook = ddp_comm_hook
self._ddp_comm_wrapper = ddp_comm_wrapper
self._pids: Optional[List[int]] = None
self._sync_dir: Optional[str] = None
self._rank_0_has_called_call_children_scripts: bool = False
self.set_world_ranks()
@property
def is_distributed(self) -> bool:
return True
@property
def root_device(self) -> torch.device:
return self.parallel_devices[self.local_rank]
@property
def num_nodes(self) -> int:
return self._num_nodes
@num_nodes.setter
def num_nodes(self, num_nodes: int) -> None:
# note that world ranks is related to num_nodes, when resetting it, need to reset world ranks
self._num_nodes = num_nodes
self.set_world_ranks()
@property
def sync_batchnorm(self) -> bool:
return self._sync_batchnorm
@sync_batchnorm.setter
def sync_batchnorm(self, sync_batchnorm: bool) -> None:
self._sync_batchnorm = sync_batchnorm
@property
def task_idx(self) -> Optional[int]:
rank_zero_deprecation(
f"`{self.__class__.__name__}.task_idx` is deprecated in v1.4 and will be removed in v1.6. Use "
f"`{self.__class__.__name__}.local_rank` instead."
)
return self._task_idx
@task_idx.setter
def task_idx(self, task_idx: int) -> None:
self._task_idx = task_idx
@property
def distributed_sampler_kwargs(self):
distributed_sampler_kwargs = dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank)
return distributed_sampler_kwargs
@property
def _is_single_process_single_device(self) -> bool:
return True
[docs] def setup_environment(self) -> None:
# start the other scripts
if not self.cluster_environment.creates_children():
self._call_children_scripts()
# set the task idx
self.task_idx = self.cluster_environment.local_rank()
self.setup_distributed()
def _call_children_scripts(self):
# bookkeeping of spawned processes
assert self.local_rank == 0
self._check_can_spawn_children()
self._has_spawned_children = True
# DDP Environment variables
os.environ["MASTER_ADDR"] = self.cluster_environment.master_address()
os.environ["MASTER_PORT"] = str(self.cluster_environment.master_port())
# allow the user to pass the node rank
os.environ["NODE_RANK"] = str(self.cluster_environment.node_rank())
os.environ["LOCAL_RANK"] = str(self.cluster_environment.local_rank())
# Check if the current calling command looked like `python a/b/c.py` or `python -m a.b.c`
# See https://docs.python.org/3/reference/import.html#main-spec
if __main__.__spec__ is None: # pragma: no-cover
# Script called as `python a/b/c.py`
# when user is using hydra find the absolute path
path_lib = os.path.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:
full_path = os.path.abspath(command[0])
command[0] = full_path
# use the same python interpreter and actually running
command = [sys.executable] + command
else: # Script called as `python -m a.b.c`
command = [sys.executable, "-m", __main__.__spec__.name] + sys.argv[1:]
# 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.parallel_devices is None:
raise MisconfigurationException("you selected (distribute_backend = ddp) but did not set Trainer(gpus=?)")
os.environ["WORLD_SIZE"] = f"{self.num_processes * self.num_nodes}"
self.interactive_ddp_procs = []
for local_rank in range(1, self.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()
os_cwd = f'"{os.getcwd()}"'
command += [f"hydra.run.dir={os_cwd}", f"hydra.job.name=train_ddp_process_{local_rank}"]
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)
self._rank_0_has_called_call_children_scripts = True
def setup_distributed(self):
reset_seed()
# determine which process we are and world size
self.set_world_ranks()
# set warning rank
rank_zero_only.rank = self.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
self.init_ddp_connection()
# set the ranks and devices
self.dist.rank = self.global_rank
self.dist.device = self.root_device
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) -> None:
if self.cluster_environment is None:
return
self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
rank_zero_only.rank = self.cluster_environment.global_rank()
def pre_configure_ddp(self):
# if unset, default `find_unused_parameters` `True`
# Many models require setting this parameter to True, as there are corner cases
# when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True.
# This flag does come with a performance hit, so it is suggested to disable in cases where it is possible.
self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True)
# todo: PyTorch 1.7.0 DDP introduces `self.reducer._rebuild_buckets()` breaking manual_optimization
if (
_TORCH_GREATER_EQUAL_1_7
and not self.lightning_module.automatic_optimization
and not self._ddp_kwargs.get("find_unused_parameters", False)
):
rank_zero_warn(
"From PyTorch 1.7.0, Lightning ``manual_optimization`` needs to set ``find_unused_parameters=True`` "
"to properly work with DDP."
)
self._ddp_kwargs["find_unused_parameters"] = True
def _register_ddp_hooks(self) -> None:
# In 1.8, DDP communication hooks only work with NCCL backend and SPSD (single process single device) mode
# Since 1.9, DDP communication hooks can work on all backends.
if _TORCH_GREATER_EQUAL_1_9 or (
_TORCH_GREATER_EQUAL_1_8 and self.on_gpu and self._is_single_process_single_device
):
register_ddp_comm_hook(
model=self._model,
ddp_comm_state=self._ddp_comm_state,
ddp_comm_hook=self._ddp_comm_hook,
ddp_comm_wrapper=self._ddp_comm_wrapper,
)
def configure_ddp(self):
self.pre_configure_ddp()
self._model = DistributedDataParallel(
LightningDistributedModule(self.model), device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs
)
self._register_ddp_hooks()
def determine_ddp_device_ids(self):
if self.root_device.type == "cpu":
return None
return [self.root_device.index]
def init_ddp_connection(self, global_rank: Optional[int] = None, world_size: Optional[int] = None) -> None:
global_rank = global_rank if global_rank is not None else self.cluster_environment.global_rank()
world_size = world_size if world_size is not None else self.cluster_environment.world_size()
os.environ["MASTER_ADDR"] = self.cluster_environment.master_address()
os.environ["MASTER_PORT"] = str(self.cluster_environment.master_port())
if torch.distributed.is_available() and not torch.distributed.is_initialized():
log.info(f"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank + 1}/{world_size}")
torch.distributed.init_process_group(
self.torch_distributed_backend, rank=global_rank, world_size=world_size
)
# on rank=0 let everyone know training is starting
rank_zero_info(
f"{'-' * 100}\n"
f"distributed_backend={self.torch_distributed_backend}\n"
f"All DDP processes registered. Starting ddp with {self.world_size} processes\n"
f"{'-' * 100}\n"
)
[docs] def pre_dispatch(self):
# share ddp pids to all processes
self._rank_0_has_called_call_children_scripts = self.broadcast(self._rank_0_has_called_call_children_scripts)
if self._should_run_deadlock_detection():
self._share_information_to_prevent_deadlock()
# move the model to the correct device
self.model_to_device()
if self.sync_batchnorm:
self.model = self.configure_sync_batchnorm(self.model)
self.configure_ddp()
[docs] def barrier(self, *args, **kwargs) -> None:
if not distributed_available():
return
if _TORCH_GREATER_EQUAL_1_8 and torch.distributed.get_backend() == "nccl":
torch.distributed.barrier(device_ids=self.determine_ddp_device_ids())
else:
torch.distributed.barrier()
[docs] def pre_backward(self, closure_loss: torch.Tensor) -> None:
"""Run before precision plugin executes backward"""
if not self.lightning_module.automatic_optimization:
prepare_for_backward(self.model, closure_loss)
[docs] def reduce(self, tensor, group: Optional[Any] = None, reduce_op: Union[ReduceOp, str] = "mean") -> torch.Tensor:
"""
Reduces a tensor from several distributed processes to one aggregated tensor.
Args:
tensor: the tensor to sync and reduce
group: the process group to gather results from. Defaults to all processes (world)
reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
Can also be a string 'sum' to calculate the sum during reduction.
Return:
reduced value, except when the input was not a tensor the output remains is unchanged
"""
if isinstance(tensor, torch.Tensor):
tensor = sync_ddp_if_available(tensor, group, reduce_op=reduce_op)
return tensor
def training_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def validation_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def test_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def predict_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def post_training_step(self):
if not self.lightning_module.automatic_optimization:
self.model.require_backward_grad_sync = True
@classmethod
def register_plugins(cls, plugin_registry: Dict) -> None:
plugin_registry.register(
"ddp_find_unused_parameters_false",
cls,
description="DDP Plugin with `find_unused_parameters` as False",
find_unused_parameters=False,
)
def _should_run_deadlock_detection(self) -> bool:
"""Determines whether the plugin will perform process reconciliation in case of errors.
If the environment variable `PL_RECONCILE_PROCESS` is set, run detection regardless of the cluster environment.
By default this is disabled. Otherwise, if the cluster environment creates the processes, allow the scheduler /
parent process to perform the process termination, external to Lightning.
"""
return os.getenv("PL_RECONCILE_PROCESS", "0") == "1" or self._rank_0_has_called_call_children_scripts
def _share_information_to_prevent_deadlock(self) -> None:
self._share_pids()
# there should be a unique sync_dir per nodes.
if self.local_rank == 0:
# create a temporary directory used to synchronize processes on deadlock.
self._sync_dir = tempfile.mkdtemp()
sync_dirs = []
global_node_rank_zero = 0
for _ in range(self.num_nodes):
sync_dirs.append(self.broadcast(self._sync_dir, global_node_rank_zero))
global_node_rank_zero += self.world_size // self.num_nodes
self._sync_dir = sync_dirs[self.node_rank]
def _share_pids(self) -> None:
"""Make all DDP processes aware of all processes pids."""
self.barrier()
pids = self.all_gather(torch.tensor(os.getpid(), device=self.root_device))
pids = pids.cpu().numpy().tolist()
self._pids = pids if isinstance(pids, list) else [pids]
[docs] def reconciliate_processes(self, trace: str) -> None:
if self.world_size < 2:
return
if not self._should_run_deadlock_detection():
return
sync_dir = self._sync_dir
if not sync_dir:
rank_zero_warn("Error handling mechanism for deadlock detection is uninitialized. Skipping check.")
return
# The cluster may be configured to periodically purge the `/tmp`
# directory, in which case `sync_dir` may not exist anymore at this
# point. Idempotently create it to ensure its existence.
Path(sync_dir).mkdir(parents=True, exist_ok=True)
# save a file locally.
torch.save(True, os.path.join(sync_dir, f"{self.global_rank}.pl"))
# sleep for a short time
time.sleep(3)
# return if all processes wrote a file in the `sync_dir`.
# todo (tchaton) Add support for non-shared file-system which will fail.
if len(os.listdir(sync_dir)) == (self.world_size // self.num_nodes):
return
for pid in self._pids:
if pid != os.getpid():
os.kill(pid, signal.SIGKILL)
shutil.rmtree(sync_dir)
raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
