venv/lib/python3.11/site-packages/lightning_fabric/strategies/xla.py

# 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__)