515 lines
22 KiB
Python
515 lines
22 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import shutil
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import signal
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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from time import sleep
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from typing import Any, Dict, List, Optional, Union
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import __main__
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import numpy as np
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import torch
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import torch.distributed
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from torch.nn import Module
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from torch.nn.parallel.distributed import DistributedDataParallel
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import pytorch_lightning as pl
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from pytorch_lightning.core.optimizer import _convert_to_lightning_optimizers, LightningOptimizer
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from pytorch_lightning.overrides import LightningDistributedModule
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from pytorch_lightning.overrides.distributed import prepare_for_backward
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from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
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from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
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from pytorch_lightning.plugins.precision import PrecisionPlugin
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from pytorch_lightning.strategies.parallel import ParallelStrategy
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities import (
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_FAIRSCALE_AVAILABLE,
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_HYDRA_AVAILABLE,
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_IS_WINDOWS,
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_TORCH_GREATER_EQUAL_1_8,
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_TORCH_GREATER_EQUAL_1_9,
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_TORCH_GREATER_EQUAL_1_10,
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rank_zero_warn,
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)
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from pytorch_lightning.utilities.distributed import _revert_sync_batchnorm, distributed_available
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from pytorch_lightning.utilities.distributed import group as _group
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from pytorch_lightning.utilities.distributed import (
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init_dist_connection,
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rank_zero_only,
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ReduceOp,
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sync_ddp_if_available,
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)
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from pytorch_lightning.utilities.enums import _StrategyType
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from pytorch_lightning.utilities.exceptions import DeadlockDetectedException, MisconfigurationException
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from pytorch_lightning.utilities.seed import reset_seed
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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if _FAIRSCALE_AVAILABLE:
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from fairscale.optim import OSS
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if _HYDRA_AVAILABLE:
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from hydra.core.hydra_config import HydraConfig
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from hydra.utils import get_original_cwd, to_absolute_path
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if _TORCH_GREATER_EQUAL_1_8:
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from pytorch_lightning.utilities.distributed import register_ddp_comm_hook
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log = logging.getLogger(__name__)
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class DDPStrategy(ParallelStrategy):
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"""Plugin for multi-process single-device training on one or multiple nodes.
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The main process in each node spawns N-1 child processes via :func:`subprocess.Popen`, where N is the number of
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devices (e.g. GPU) per node. It is very similar to how :mod:`torch.distributed.launch` launches processes.
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"""
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distributed_backend = _StrategyType.DDP
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def __init__(
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self,
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accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
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parallel_devices: Optional[List[torch.device]] = None,
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cluster_environment: Optional[ClusterEnvironment] = None,
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checkpoint_io: Optional[CheckpointIO] = None,
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precision_plugin: Optional[PrecisionPlugin] = None,
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ddp_comm_state: Optional[object] = None,
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ddp_comm_hook: Optional[callable] = None,
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ddp_comm_wrapper: Optional[callable] = None,
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model_averaging_period: Optional[int] = None,
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**kwargs: Union[Any, Dict[str, Any]],
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) -> None:
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super().__init__(
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accelerator=accelerator,
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parallel_devices=parallel_devices,
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cluster_environment=cluster_environment,
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checkpoint_io=checkpoint_io,
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precision_plugin=precision_plugin,
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)
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self.interactive_ddp_procs = []
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self._num_nodes = 1
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self.sync_batchnorm = False
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self.num_processes = len(self.parallel_devices) if self.parallel_devices is not None else 0
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self._ddp_kwargs = kwargs
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self._ddp_comm_state = ddp_comm_state
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self._ddp_comm_hook = ddp_comm_hook
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self._ddp_comm_wrapper = ddp_comm_wrapper
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self._model_averaging_period = model_averaging_period
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self._pids: Optional[List[int]] = None
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self._sync_dir: Optional[str] = None
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self._rank_0_has_called_call_children_scripts: bool = False
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self.set_world_ranks()
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@property
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def is_distributed(self) -> bool:
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return True
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@property
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def root_device(self) -> torch.device:
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return self.parallel_devices[self.local_rank]
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@property
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def num_nodes(self) -> int:
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return self._num_nodes
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@num_nodes.setter
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def num_nodes(self, num_nodes: int) -> None:
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# note that world ranks is related to num_nodes, when resetting it, need to reset world ranks
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self._num_nodes = num_nodes
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self.set_world_ranks()
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@property
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def distributed_sampler_kwargs(self):
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distributed_sampler_kwargs = dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank)
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return distributed_sampler_kwargs
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@property
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def _is_single_process_single_device(self) -> bool:
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return True
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def setup_environment(self) -> None:
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# start the other scripts
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if not self.cluster_environment.creates_processes_externally:
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self._call_children_scripts()
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self.setup_distributed()
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super().setup_environment()
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def setup(self, trainer: "pl.Trainer") -> None:
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super().setup(trainer)
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# share ddp pids to all processes
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self._rank_0_has_called_call_children_scripts = self.broadcast(self._rank_0_has_called_call_children_scripts)
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if self._should_run_deadlock_detection():
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self._share_information_to_prevent_deadlock()
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# move the model to the correct device
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self.model_to_device()
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if self.sync_batchnorm:
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self.model = self.configure_sync_batchnorm(self.model)
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# skip wrapping the model if we are not fitting as no gradients need to be exchanged
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trainer_fn = self.lightning_module.trainer.state.fn
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if trainer_fn == TrainerFn.FITTING:
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self.configure_ddp()
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def _setup_model(self, model: Module) -> DistributedDataParallel:
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"""Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
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return DistributedDataParallel(module=model, device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs)
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def _call_children_scripts(self):
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# bookkeeping of spawned processes
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self._check_can_spawn_children()
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# DDP Environment variables
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os.environ["MASTER_ADDR"] = self.cluster_environment.main_address
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os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port)
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# allow the user to pass the node rank
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os.environ["NODE_RANK"] = str(self.cluster_environment.node_rank())
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os.environ["LOCAL_RANK"] = str(self.cluster_environment.local_rank())
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# Check if the current calling command looked like `python a/b/c.py` or `python -m a.b.c`
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# See https://docs.python.org/3/reference/import.html#main-spec
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if __main__.__spec__ is None: # pragma: no-cover
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# Script called as `python a/b/c.py`
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# when user is using hydra find the absolute path
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path_lib = os.path.abspath if not _HYDRA_AVAILABLE else to_absolute_path
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# pull out the commands used to run the script and resolve the abs file path
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command = sys.argv
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try:
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full_path = path_lib(command[0])
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except Exception:
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full_path = os.path.abspath(command[0])
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command[0] = full_path
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# use the same python interpreter and actually running
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command = [sys.executable] + command
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else: # Script called as `python -m a.b.c`
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command = [sys.executable, "-m", __main__.__spec__.name] + sys.argv[1:]
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# the visible devices tell us how many GPUs we want to use.
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# when the trainer script was called the device has already been scoped by the time
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# code reaches this point. so, to call the scripts, we need to leave cuda visible devices alone
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# but forward the GPUs selected via environment variables
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if self.parallel_devices is None:
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raise MisconfigurationException("you selected (distribute_backend = ddp) but did not set Trainer(gpus=?)")
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os.environ["WORLD_SIZE"] = f"{self.num_processes * self.num_nodes}"
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self.interactive_ddp_procs = []
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for local_rank in range(1, self.num_processes):
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env_copy = os.environ.copy()
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env_copy["LOCAL_RANK"] = f"{local_rank}"
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# remove env var if global seed not set
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if os.environ.get("PL_GLOBAL_SEED") is None and "PL_GLOBAL_SEED" in env_copy:
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del env_copy["PL_GLOBAL_SEED"]
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# start process
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# if hydra is available and initialized, make sure to set the cwd correctly
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cwd: Optional[str] = None
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if _HYDRA_AVAILABLE:
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if HydraConfig.initialized():
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cwd = get_original_cwd()
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os_cwd = f'"{os.getcwd()}"'
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command += [f"hydra.run.dir={os_cwd}", f"hydra.job.name=train_ddp_process_{local_rank}"]
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proc = subprocess.Popen(command, env=env_copy, cwd=cwd)
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self.interactive_ddp_procs.append(proc)
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# starting all processes at once can cause issues
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# with dataloaders delay between 1-10 seconds
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delay = np.random.uniform(1, 5, 1)[0]
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sleep(delay)
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self._rank_0_has_called_call_children_scripts = True
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def setup_distributed(self):
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reset_seed()
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# determine which process we are and world size
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self.set_world_ranks()
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# set warning rank
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rank_zero_only.rank = self.global_rank
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# set up server using proc 0's ip address
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# try to init for 20 times at max in case ports are taken
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# where to store ip_table
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init_dist_connection(self.cluster_environment, self.torch_distributed_backend)
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def _check_can_spawn_children(self):
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if self.local_rank != 0:
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raise RuntimeError(
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"Lightning attempted to launch new distributed processes with `local_rank > 0`. This should not happen."
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" Possible reasons: 1) LOCAL_RANK environment variable was incorrectly modified by the user,"
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" 2) `ClusterEnvironment.creates_processes_externally` incorrectly implemented."
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)
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def set_world_ranks(self) -> None:
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if self.cluster_environment is None:
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return
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self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
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self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
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rank_zero_only.rank = self.cluster_environment.global_rank()
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def pre_configure_ddp(self):
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# if unset, default `find_unused_parameters` `True`
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# Many models require setting this parameter to True, as there are corner cases
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# when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True.
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# This flag does come with a performance hit, so it is suggested to disable in cases where it is possible.
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self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True)
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if not self.lightning_module.automatic_optimization and not self._ddp_kwargs.get(
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"find_unused_parameters", False
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):
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# TODO: PyTorch 1.7.0 DDP introduces `self.reducer._rebuild_buckets()` breaking manual_optimization
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rank_zero_warn(
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"From PyTorch 1.7.0, Lightning `manual_optimization` needs to set `find_unused_parameters=True` to"
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" properly work with DDP. Using `find_unused_parameters=True`."
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)
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self._ddp_kwargs["find_unused_parameters"] = True
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def _register_ddp_hooks(self) -> None:
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# In 1.8, DDP communication hooks only work with NCCL backend and SPSD (single process single device) mode
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# Since 1.9, DDP communication hooks can work on all backends.
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if _TORCH_GREATER_EQUAL_1_9 or (
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_TORCH_GREATER_EQUAL_1_8 and self.on_gpu and self._is_single_process_single_device
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):
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register_ddp_comm_hook(
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model=self.model,
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ddp_comm_state=self._ddp_comm_state,
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ddp_comm_hook=self._ddp_comm_hook,
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ddp_comm_wrapper=self._ddp_comm_wrapper,
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)
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if _TORCH_GREATER_EQUAL_1_10 and self.lightning_module.trainer.state.fn == TrainerFn.FITTING:
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import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
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if isinstance(self._ddp_comm_state, post_localSGD.PostLocalSGDState):
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self._reinit_optimizers_with_post_localSGD(self._ddp_comm_state.start_localSGD_iter)
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def _reinit_optimizers_with_post_localSGD(self, warmup_steps: int):
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optimizers = self.lightning_module.trainer.optimizers
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if self._model_averaging_period is None:
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raise ValueError(
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"Post-localSGD algorithm is used, but model averaging period is not provided to DDP strategy."
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)
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if _TORCH_GREATER_EQUAL_1_10:
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if not _IS_WINDOWS:
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from torch.distributed.optim import DistributedOptimizer
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import torch.distributed.algorithms.model_averaging.averagers as averagers
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from torch.distributed.optim import PostLocalSGDOptimizer, ZeroRedundancyOptimizer
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averager = averagers.PeriodicModelAverager(period=self._model_averaging_period, warmup_steps=warmup_steps)
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for x, optimizer in enumerate(optimizers):
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if isinstance(optimizer, LightningOptimizer):
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optimizer = optimizer._optimizer
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is_distributed_optimizer = isinstance(optimizer, DistributedOptimizer) if not _IS_WINDOWS else False
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if (
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is_distributed_optimizer
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or isinstance(optimizer, ZeroRedundancyOptimizer)
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or (_FAIRSCALE_AVAILABLE and isinstance(optimizer, OSS))
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):
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raise ValueError(
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f"Cannot wrap a distributed optimizer of type {optimizer.__name__} by PostLocalSGDOptimizer."
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)
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if isinstance(optimizer, PostLocalSGDOptimizer):
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continue
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optim_class = type(optimizer)
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post_localSGD_optimizer = PostLocalSGDOptimizer(
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params=optimizer.param_groups,
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optimizer_class=optim_class,
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averager=averager,
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**optimizer.defaults,
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)
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optimizers[x] = post_localSGD_optimizer
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del optimizer
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trainer = self.lightning_module.trainer
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trainer.optimizers = optimizers
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_convert_to_lightning_optimizers(trainer)
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def configure_ddp(self) -> None:
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self.pre_configure_ddp()
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self.model = self._setup_model(LightningDistributedModule(self.model))
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self._register_ddp_hooks()
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def determine_ddp_device_ids(self):
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if self.root_device.type == "cpu":
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return None
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return [self.root_device.index]
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def barrier(self, *args, **kwargs) -> None:
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if not distributed_available():
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return
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if _TORCH_GREATER_EQUAL_1_8 and torch.distributed.get_backend() == "nccl":
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torch.distributed.barrier(device_ids=self.determine_ddp_device_ids())
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else:
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torch.distributed.barrier()
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def broadcast(self, obj: object, src: int = 0) -> object:
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obj = [obj]
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if self.global_rank != src:
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obj = [None]
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torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
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return obj[0]
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def pre_backward(self, closure_loss: torch.Tensor) -> None:
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"""Run before precision plugin executes backward."""
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if not self.lightning_module.automatic_optimization:
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prepare_for_backward(self.model, closure_loss)
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def model_to_device(self):
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self.model.to(self.root_device)
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def reduce(self, tensor, group: Optional[Any] = None, reduce_op: Union[ReduceOp, str] = "mean") -> torch.Tensor:
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"""Reduces a tensor from several distributed processes to one aggregated tensor.
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Args:
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tensor: the tensor to sync and reduce
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group: the process group to gather results from. Defaults to all processes (world)
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reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
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Can also be a string 'sum' to calculate the sum during reduction.
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Return:
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reduced value, except when the input was not a tensor the output remains is unchanged
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"""
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if isinstance(tensor, torch.Tensor):
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tensor = sync_ddp_if_available(tensor, group, reduce_op=reduce_op)
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return tensor
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def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.train_step_context():
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return self.model(*args, **kwargs)
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def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.val_step_context():
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if isinstance(self.model, DistributedDataParallel):
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# used when calling `trainer.fit`
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return self.model(*args, **kwargs)
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else:
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# used when calling `trainer.validate`
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return self.lightning_module.validation_step(*args, **kwargs)
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def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.test_step_context():
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return self.lightning_module.test_step(*args, **kwargs)
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def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.predict_step_context():
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return self.lightning_module.predict_step(*args, **kwargs)
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def post_training_step(self):
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if not self.lightning_module.automatic_optimization:
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self.model.require_backward_grad_sync = True
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@classmethod
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def register_plugins(cls, plugin_registry: Dict) -> None:
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plugin_registry.register(
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"ddp_find_unused_parameters_false",
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cls,
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description="DDP Strategy with `find_unused_parameters` as False",
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find_unused_parameters=False,
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)
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def _should_run_deadlock_detection(self) -> bool:
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"""Determines whether the plugin will perform process reconciliation in case of errors.
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If the environment variable `PL_RECONCILE_PROCESS` is set, run detection regardless of the cluster environment.
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By default this is disabled. Otherwise, if the cluster environment creates the processes, allow the scheduler /
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parent process to perform the process termination, external to Lightning.
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"""
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return os.getenv("PL_RECONCILE_PROCESS", "0") == "1" or self._rank_0_has_called_call_children_scripts
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def _share_information_to_prevent_deadlock(self) -> None:
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self._share_pids()
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# there should be a unique sync_dir per nodes.
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if self.local_rank == 0:
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# create a temporary directory used to synchronize processes on deadlock.
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self._sync_dir = tempfile.mkdtemp()
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sync_dirs = []
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global_node_rank_zero = 0
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for _ in range(self.num_nodes):
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sync_dirs.append(self.broadcast(self._sync_dir, global_node_rank_zero))
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global_node_rank_zero += self.world_size // self.num_nodes
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self._sync_dir = sync_dirs[self.node_rank]
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def _share_pids(self) -> None:
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"""Make all DDP processes aware of all processes pids."""
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self.barrier()
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pids = self.all_gather(torch.tensor(os.getpid(), device=self.root_device))
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pids = pids.cpu().numpy().tolist()
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self._pids = pids if isinstance(pids, list) else [pids]
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def reconciliate_processes(self, trace: str) -> None:
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if self.world_size < 2:
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return
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if not self._should_run_deadlock_detection():
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return
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|
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sync_dir = self._sync_dir
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|
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if not sync_dir:
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rank_zero_warn("Error handling mechanism for deadlock detection is uninitialized. Skipping check.")
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return
|
|
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# The cluster may be configured to periodically purge the `/tmp`
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# directory, in which case `sync_dir` may not exist anymore at this
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# point. Idempotently create it to ensure its existence.
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Path(sync_dir).mkdir(parents=True, exist_ok=True)
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# save a file locally.
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torch.save(True, os.path.join(sync_dir, f"{self.global_rank}.pl"))
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|
|
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# sleep for a short time
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time.sleep(3)
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|
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|
# return if all processes wrote a file in the `sync_dir`.
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# todo (tchaton) Add support for non-shared file-system which will fail.
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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)
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|
raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
|
|
|
|
def teardown(self) -> None:
|
|
super().teardown()
|
|
if isinstance(self.model, DistributedDataParallel):
|
|
self.model = self.lightning_module
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|
|
|
if self.sync_batchnorm:
|
|
self.model = _revert_sync_batchnorm(self.model)
|
|
|
|
if self.on_gpu:
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|
# GPU teardown
|
|
self.lightning_module.cpu()
|
|
# clean up memory
|
|
torch.cuda.empty_cache()
|