Source code for pytorch_lightning.strategies.hpu_parallel
# Copyright The Lightning AI 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, Callable, Dict, List, Optional, Union
import torch.distributed
from torch.nn import Module
from torch.optim.optimizer import Optimizer
import pytorch_lightning as pl
from lightning_fabric.plugins import CheckpointIO, ClusterEnvironment
from lightning_fabric.utilities.distributed import group as _group
from pytorch_lightning.accelerators.hpu import _HPU_AVAILABLE
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.plugins.io.hpu_plugin import HPUCheckpointIO
from pytorch_lightning.plugins.io.wrapper import _WrappingCheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _TORCH_LESSER_EQUAL_1_10_2
from pytorch_lightning.utilities.types import STEP_OUTPUT
if _HPU_AVAILABLE:
import habana_frameworks.torch.core as htcore
import habana_frameworks.torch.distributed.hccl # noqa: F401
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"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
ddp_comm_state: Optional[object] = None,
ddp_comm_hook: Optional[Callable] = None,
ddp_comm_wrapper: Optional[Callable] = None,
model_averaging_period: Optional[int] = None,
process_group_backend: Optional[str] = "hccl",
**kwargs: Any,
) -> None:
if not _HPU_AVAILABLE:
raise MisconfigurationException("`HPUParallelStrategy` requires HPU devices to run")
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
ddp_comm_state=ddp_comm_state,
ddp_comm_hook=ddp_comm_hook,
ddp_comm_wrapper=ddp_comm_wrapper,
model_averaging_period=model_averaging_period,
process_group_backend=process_group_backend,
**kwargs,
)
@property
def checkpoint_io(self) -> CheckpointIO:
if self._checkpoint_io is None:
self._checkpoint_io = HPUCheckpointIO()
elif isinstance(self._checkpoint_io, _WrappingCheckpointIO):
self._checkpoint_io.checkpoint_io = HPUCheckpointIO()
return self._checkpoint_io
@checkpoint_io.setter
def checkpoint_io(self, io: Optional[CheckpointIO]) -> None:
self._checkpoint_io = io
[docs] def setup_environment(self) -> None:
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) -> None:
# 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]
_hpu_broadcast_object_list(obj, src, group=_group.WORLD)
return obj[0]
def on_after_backward(self) -> None:
# Break lazy accumulation of graph after fwd+bwd
htcore.mark_step()
[docs] def optimizer_step(
self,
optimizer: Optimizer,
opt_idx: int,
closure: Callable[[], Any],
model: Optional[Union["pl.LightningModule", Module]] = None,
**kwargs: Any,
) -> Any:
optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
# Break lazy accumulation of graph after optimizer
htcore.mark_step()
return optimizer_output
def validation_step_end(self, step_output: STEP_OUTPUT) -> STEP_OUTPUT:
# Break lazy accumulation of graph after every step
htcore.mark_step()
return step_output
def test_step_end(self, step_output: STEP_OUTPUT) -> STEP_OUTPUT:
# Break lazy accumulation of graph after every step
htcore.mark_step()
return step_output
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
[docs] def teardown(self) -> None:
super().teardown()
# Was set to local rank
os.environ.pop("ID", None)
os.environ.pop("HCCL_DISTRIBUTED_BACKEND", None)
# The code underneath is taken from PyTorch `torch/distributed/distributed_c10d.py`
# the distributed backend and tensor type updates for habana backend is done here before broadcast
def _hpu_broadcast_object_list(object_list, src=0, group=None, device=None): # type: ignore
from torch.distributed import _rank_not_in_group, Backend, broadcast, get_backend, get_rank
from torch.distributed.distributed_c10d import _object_to_tensor, _tensor_to_object
if _rank_not_in_group(group):
return
my_rank = get_rank()
# Serialize object_list elements to tensors on src rank.
if my_rank == src:
tensor_list, size_list = zip(*[_object_to_tensor(obj, device) for obj in object_list])
object_sizes_tensor = torch.cat(size_list)
else:
object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
# Current device selection.
# To preserve backwards compatibility, ``device`` is default to ``None``
# in which case we run current logic of device selection, i.e.
# ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
# case it is not ``None`` we move the size and object tensors to be
# broadcasted to this device.
group_backend = get_backend(group)
is_nccl_backend = group_backend == Backend.NCCL
is_hpu_backend = os.environ.get("HCCL_DISTRIBUTED_BACKEND") == "1"
if device is not None:
if is_nccl_backend and device.type != "cuda":
raise ValueError("device type must be cuda for nccl backend")
current_device = device
else:
current_device = torch.device("cpu")
if is_nccl_backend:
# See note about using torch.cuda.current_device() here in
# docstring. We cannot simply use my_rank since rank == device is
# not necessarily true.
current_device = torch.device("cuda", torch.cuda.current_device())
if is_nccl_backend:
object_sizes_tensor = object_sizes_tensor.to(current_device)
elif is_hpu_backend:
current_device = torch.device("hpu")
# Workaround: HPU doesn't not support long tensors for collectives
if (object_sizes_tensor.type() == "torch.LongTensor") or (object_sizes_tensor.type() == "torch.hpu.LongTensor"):
object_sizes_tensor = object_sizes_tensor.int()
else:
print("unhandled hpu object_sizes_tensor type :: ", object_sizes_tensor.type())
object_sizes_tensor = object_sizes_tensor.to(current_device)
# Broadcast object sizes
broadcast(object_sizes_tensor, src=src, group=group)
# Concatenate and broadcast serialized object tensors
if my_rank == src:
object_tensor = torch.cat(tensor_list)
else:
object_tensor = torch.empty(
torch.sum(object_sizes_tensor).int().item(),
dtype=torch.uint8,
)
if is_nccl_backend or is_hpu_backend:
object_tensor = object_tensor.to(current_device)
broadcast(object_tensor, src=src, group=group)
# Deserialize objects using their stored sizes.
offset = 0
if my_rank != src:
for i, obj_size in enumerate(object_sizes_tensor):
obj_view = object_tensor[offset : offset + obj_size]
obj_view = obj_view.type(torch.uint8)
if obj_view.device != torch.device("cpu"):
obj_view = obj_view.cpu()
offset += obj_size
object_list[i] = _tensor_to_object(obj_view, obj_size)
