Source code for pytorch_lightning.strategies.hpu_parallel
# 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 Dict, List, Optional
import torch
import torch.distributed
import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.overrides.torch_distributed import broadcast_object_list
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.io.hpu_plugin import HPUCheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.utilities.distributed import group as _group
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _HPU_AVAILABLE, _TORCH_LESSER_EQUAL_1_10_2
if _HPU_AVAILABLE:
import habana_frameworks.torch.core.hccl # noqa: F401
from habana_frameworks.torch.utils.library_loader import load_habana_module
log = logging.getLogger(__name__)
[docs]class HPUParallelStrategy(DDPStrategy):
"""Strategy for distributed training on multiple HPU devices."""
strategy_name = "hpu_parallel"
def __init__(
self,
accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
process_group_backend: Optional[str] = "hccl",
) -> None:
if not _HPU_AVAILABLE:
raise MisconfigurationException("`HPUParallelStrategy` requires HPU devices to run")
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
checkpoint_io=checkpoint_io or HPUCheckpointIO(),
precision_plugin=precision_plugin,
process_group_backend=process_group_backend,
)
[docs] def setup_environment(self) -> None:
# This function is used to load Habana libraries required for PyTorch
# to register HPU as one of the available devices.
load_habana_module()
os.environ["ID"] = str(self.local_rank)
if self._process_group_backend == "hccl":
# this env is used in overrides to check the backend initiated
os.environ["HCCL_DISTRIBUTED_BACKEND"] = str(1)
super().setup_environment()
def determine_ddp_device_ids(self) -> None:
return None
def pre_configure_ddp(self): # type: ignore
# 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)
self._static_graph = False
static_graph = self._ddp_kwargs.get("static_graph")
if static_graph:
# when _set_static_graph() is called find_unused_parameters does not have any significance.
# Resetting the value of find_unused_parameters to False which is the default value to DDP
self._ddp_kwargs["find_unused_parameters"] = False
self._static_graph = True
if static_graph is not None:
# DDP does not accept static_graph as a parameter, hence removing it from the list
del self._ddp_kwargs["static_graph"]
def configure_ddp(self) -> None:
# DDP does not accept static graph as param with torch < 1.11
if _TORCH_LESSER_EQUAL_1_10_2:
log.detail(f"{self.__class__.__name__}: configuring DistributedDataParallel")
self.pre_configure_ddp()
self.model = self._setup_model(LightningDistributedModule(self.model)) # type: ignore
if self.root_device.type == "hpu" and self._static_graph:
self._model._set_static_graph() # type: ignore
self._register_ddp_hooks()
else:
super().configure_ddp()
[docs] def broadcast(self, obj: object, src: int = 0) -> object: # type: ignore
obj = [obj]
if self.global_rank != src:
obj = [None]
broadcast_object_list(obj, src, group=_group.WORLD)
return obj[0]
[docs] def teardown(self) -> None:
log.detail(f"{self.__class__.__name__}: tearing down strategy.")
super().teardown()
log.detail(f"{self.__class__.__name__}: moving model to CPU")
self.lightning_module.cpu() # type: ignore
# Was set to local rank
os.environ.pop("ID", None)
os.environ.pop("HCCL_DISTRIBUTED_BACKEND", None)
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)