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# 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 io
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
import torch
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer
from torch.utils.data import DataLoader
from typing_extensions import override
from lightning_fabric.accelerators import Accelerator
from lightning_fabric.accelerators.xla import _XLA_GREATER_EQUAL_2_1
from lightning_fabric.plugins import CheckpointIO, Precision, XLAPrecision
from lightning_fabric.plugins.environments import XLAEnvironment
from lightning_fabric.plugins.io.xla import XLACheckpointIO
from lightning_fabric.strategies import ParallelStrategy, _StrategyRegistry
from lightning_fabric.strategies.launchers.xla import _XLALauncher
from lightning_fabric.strategies.strategy import TBroadcast
from lightning_fabric.utilities.rank_zero import rank_zero_only
from lightning_fabric.utilities.types import _PATH, ReduceOp
if TYPE_CHECKING:
from torch_xla.distributed.parallel_loader import MpDeviceLoader
class XLAStrategy(ParallelStrategy):
"""Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
method."""
def __init__(
self,
accelerator: Optional[Accelerator] = None,
parallel_devices: Optional[list[torch.device]] = None,
checkpoint_io: Optional[XLACheckpointIO] = None,
precision: Optional[XLAPrecision] = None,
sync_module_states: bool = True,
) -> None:
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=XLAEnvironment(),
checkpoint_io=checkpoint_io,
precision=precision,
)
self._backward_sync_control = None # XLA synchronizes gradients in the optimizer.step() call
self._launched = False
self._sync_module_states = sync_module_states
@property
@override
def root_device(self) -> torch.device:
if not self._launched:
raise RuntimeError("Accessing the XLA device before processes have spawned is not allowed.")
import torch_xla.core.xla_model as xm
return xm.xla_device()
@property
def num_processes(self) -> int:
return len(self.parallel_devices) if self.parallel_devices is not None else 0
@property
@override
def checkpoint_io(self) -> XLACheckpointIO:
plugin = self._checkpoint_io
if plugin is not None:
assert isinstance(plugin, XLACheckpointIO)
return plugin
return XLACheckpointIO()
@checkpoint_io.setter
@override
def checkpoint_io(self, io: Optional[CheckpointIO]) -> None:
if io is not None and not isinstance(io, XLACheckpointIO):
raise TypeError(f"The XLA strategy can only work with the `XLACheckpointIO` plugin, found {io}")
self._checkpoint_io = io
@property
@override
def precision(self) -> XLAPrecision:
plugin = self._precision
if plugin is not None:
assert isinstance(plugin, XLAPrecision)
return plugin
return XLAPrecision("32-true")
@precision.setter
@override
def precision(self, precision: Optional[Precision]) -> None:
if precision is not None and not isinstance(precision, XLAPrecision):
raise TypeError(f"The XLA strategy can only work with the `XLAPrecision` plugin, found {precision}")
self._precision = precision
@property
@override
def global_rank(self) -> int:
return super().global_rank if self._launched else 0
@property
@override
def local_rank(self) -> int:
return super().local_rank if self._launched else 0
@property
@override
def node_rank(self) -> int:
return super().node_rank if self._launched else 0
@property
@override
def world_size(self) -> int:
return super().world_size if self._launched else 1
@override
def _configure_launcher(self) -> None:
self._launcher = _XLALauncher(self)
@override
def setup_environment(self) -> None:
assert self.parallel_devices is not None
if len(self.parallel_devices) == 1:
# spawning only 1 device with PjRT is not supported:
# https://github.com/Lightning-AI/pytorch-lightning/pull/17408#discussion_r1170671732
raise NotImplementedError(
f"The {type(self).__name__} does not support running on a single device with the PjRT runtime."
" Try using all devices or the `SingleDeviceXLAStrategy` strategy"
)
self._launched = True
rank_zero_only.rank = self.global_rank
super().setup_environment()
@override
def setup_module(self, module: Module) -> Module:
if self._sync_module_states:
if _XLA_GREATER_EQUAL_2_1:
from torch_xla.core.xla_model import broadcast_master_param
else:
from torch_xla.experimental.pjrt import broadcast_master_param
broadcast_master_param(module)
return module
@override
def module_to_device(self, module: Module) -> None:
module.to(self.root_device)
@override
def process_dataloader(self, dataloader: DataLoader) -> "MpDeviceLoader":
from torch_xla.distributed.parallel_loader import MpDeviceLoader
if isinstance(dataloader, MpDeviceLoader):
# dataloader is already wrapped by MpDeviceLoader
return dataloader
dataloader = MpDeviceLoader(dataloader, self.root_device)
# Mimic interface to torch.utils.data.DataLoader
dataloader.dataset = dataloader._loader.dataset
dataloader.batch_sampler = getattr(dataloader._loader, "batch_sampler", None)
return dataloader
@override
def all_gather(self, tensor: Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> Tensor:
"""Function to gather a tensor from several distributed processes.
Args:
tensor: tensor to all-gather.
group: unused.
sync_grads: flag that allows users to synchronize gradients for the all-gather operation.
Return:
A tensor of shape (world_size, ...)
"""
if not self._launched:
return tensor
if not isinstance(tensor, Tensor):
raise NotImplementedError(
f"`{type(self).__name__}.all_gather` is only implemented for tensors. Given {tensor}"
)
if tensor.dim() == 0:
tensor = tensor.unsqueeze(0)
original_device = tensor.device
tensor = tensor.to(self.root_device)
import torch_xla.core.functions as xf
import torch_xla.core.xla_model as xm
tensor = xf.all_gather(tensor) if sync_grads else xm.all_gather(tensor)
tensor = tensor.to(original_device)
return tensor
@override
def all_reduce(
self, output: Union[Tensor, Any], group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None
) -> Tensor:
if not isinstance(output, Tensor):
output = torch.tensor(output, device=self.root_device)
invalid_reduce_op = isinstance(reduce_op, ReduceOp) and reduce_op != ReduceOp.SUM
invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
if invalid_reduce_op or invalid_reduce_op_str:
raise ValueError(
"Currently, the XLAStrategy only supports `sum`, `mean`, `avg` for the reduce operation, got:"
f" {reduce_op}"
)
import torch_xla.core.xla_model as xm
output = xm.mesh_reduce("reduce", output, sum)
if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
output = output / self.world_size
return output
@override
def barrier(self, name: Optional[str] = None, *args: Any, **kwargs: Any) -> None:
if not self._launched:
return
import torch_xla.core.xla_model as xm
if name is None:
# `None` is not supported: "TypeError: _xla_rendezvous(): incompatible function arguments"
name = ""
xm.rendezvous(name)
@override
def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
if not self._launched:
return obj
import torch_xla.core.xla_model as xm
is_tensor = isinstance(obj, Tensor)
if is_tensor:
if obj.dim() == 0:
obj = obj.unsqueeze(0)
original_device = obj.device
# XLA distributed requires that the data is on the XLA device
obj = obj.to(self.root_device)
else:
# support for arbitrary pickle-ables
buffer = io.BytesIO()
torch.save(obj, buffer)
obj = torch.tensor( # type: ignore[assignment]
bytearray(buffer.getbuffer()), device=self.root_device, dtype=torch.float
)
obj = [obj]
xm.collective_broadcast(obj, root_ordinal=src)
obj = obj[0]
if not is_tensor:
# this will preserve the dtype and device of any tensors
buffer = io.BytesIO(obj.cpu().byte().numpy())
obj = torch.load(buffer)
else:
obj = obj.to(original_device)
return obj
@override
def save_checkpoint(
self,
path: _PATH,
state: dict[str, Union[Module, Optimizer, Any]],
storage_options: Optional[Any] = None,
filter: Optional[dict[str, Callable[[str, Any], bool]]] = None,
) -> None:
"""Save model, optimizer, and other state as a checkpoint file.
Args:
path: A path to where the file(s) should be saved
state: A dictionary with contents to be saved. If the dict contains modules or optimizers, their
state-dict will be retrieved and converted automatically.
storage_options: Additional options for the ``CheckpointIO`` plugin
filter: An optional dictionary of the same format as ``state`` mapping keys to callables that return a
boolean indicating whether the given parameter should be saved (``True``) or filtered out (``False``).
"""
import torch_xla.core.xla_model as xm
# sync any pending lazy tensors on all ranks before saving to prevent potential collective hangs
xm.mark_step()
# save on global rank zero only
super().save_checkpoint(path, state, storage_options=storage_options, filter=filter)
@classmethod
@override
def register_strategies(cls, strategy_registry: _StrategyRegistry) -> None:
strategy_registry.register("xla", cls, description=cls.__name__)
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