Source code for lightning.pytorch.strategies.xla
# 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
import os
from typing import Any, Dict, List, Optional, TYPE_CHECKING, Union
import torch
from torch import Tensor
from torch.nn import Module
import lightning.pytorch as pl
from lightning.fabric.accelerators.tpu import _XLA_AVAILABLE
from lightning.fabric.plugins import CheckpointIO, XLACheckpointIO
from lightning.fabric.plugins.environments import XLAEnvironment
from lightning.fabric.utilities.data import has_len
from lightning.fabric.utilities.optimizer import _optimizers_to_device
from lightning.fabric.utilities.types import _PATH, ReduceOp
from lightning.pytorch.overrides.base import _LightningModuleWrapperBase
from lightning.pytorch.plugins.io.wrapper import _WrappingCheckpointIO
from lightning.pytorch.plugins.precision import PrecisionPlugin
from lightning.pytorch.strategies.ddp import DDPStrategy
from lightning.pytorch.strategies.launchers.xla import _XLALauncher
from lightning.pytorch.strategies.strategy import TBroadcast
from lightning.pytorch.trainer.states import TrainerFn
from lightning.pytorch.utilities import find_shared_parameters, set_shared_parameters
from lightning.pytorch.utilities.rank_zero import rank_zero_only
from lightning.pytorch.utilities.types import STEP_OUTPUT
if TYPE_CHECKING and _XLA_AVAILABLE:
from torch_xla.distributed.parallel_loader import MpDeviceLoader
else:
MpDeviceLoader = None
[docs]class XLAStrategy(DDPStrategy):
"""Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
method."""
strategy_name = "xla"
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
debug: bool = False,
**_: Any,
) -> None:
if not _XLA_AVAILABLE:
raise ModuleNotFoundError(str(_XLA_AVAILABLE))
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=XLAEnvironment(),
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
start_method="fork",
)
self._checkpoint_io: Optional[CheckpointIO]
self.debug = debug
self._launched = False
@property
def checkpoint_io(self) -> CheckpointIO:
if self._checkpoint_io is None:
self._checkpoint_io = XLACheckpointIO()
elif isinstance(self._checkpoint_io, _WrappingCheckpointIO):
self._checkpoint_io.checkpoint_io = XLACheckpointIO()
return self._checkpoint_io
@checkpoint_io.setter
def checkpoint_io(self, io: Optional[CheckpointIO]) -> None:
self._checkpoint_io = io
@property
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 local_rank(self) -> int:
return self.cluster_environment.local_rank() if self.cluster_environment is not None else 0
@staticmethod
def _validate_dataloader(dataloader: object) -> None:
if not has_len(dataloader):
raise TypeError(
"TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
" HINT: You can mock the length on your dataset to bypass this error."
)
[docs] def connect(self, model: "pl.LightningModule") -> None:
import torch_xla.distributed.xla_multiprocessing as xmp
self.wrapped_model = xmp.MpModelWrapper(_LightningModuleWrapperBase(model))
return super().connect(model)
def _configure_launcher(self) -> None:
self._launcher = _XLALauncher(self)
[docs] def setup(self, trainer: "pl.Trainer") -> None:
assert self.accelerator
self.accelerator.setup(trainer)
if self.debug:
os.environ["PT_XLA_DEBUG"] = "1"
assert self.lightning_module
shared_params = find_shared_parameters(self.lightning_module)
self.model_to_device()
set_shared_parameters(self.lightning_module, shared_params)
self.setup_precision_plugin()
if trainer.state.fn == TrainerFn.FITTING:
self.setup_optimizers(trainer)
_optimizers_to_device(self.optimizers, self.root_device)
def _setup_model(self, model: Module) -> Module: # type: ignore
return model
@property
def distributed_sampler_kwargs(self) -> Dict[str, int]:
return dict(num_replicas=self.world_size, rank=self.global_rank)
@property
def is_distributed(self) -> bool:
# HOST_WORLD_SIZE is not set outside the xmp.spawn process
import torch_xla.core.xla_env_vars as xenv
return (xenv.HOST_WORLD_SIZE in os.environ) and self.world_size != 1
[docs] def process_dataloader(self, dataloader: object) -> "MpDeviceLoader":
XLAStrategy._validate_dataloader(dataloader)
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
def configure_ddp(self) -> None:
pass
[docs] def barrier(self, name: Optional[str] = None, *args: Any, **kwargs: Any) -> None:
if self.is_distributed:
import torch_xla.core.xla_model as xm
xm.rendezvous(name)
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
if not self.is_distributed:
return obj
buffer = io.BytesIO()
torch.save(obj, buffer)
data = bytearray(buffer.getbuffer())
data_tensor = torch.tensor(data, device=self.root_device, dtype=torch.float)
import torch_xla.core.xla_model as xm
data = xm.all_gather(data_tensor)
buffer = io.BytesIO(data.cpu().byte().numpy())
obj = torch.load(buffer)
return obj
[docs] def 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
def setup_distributed(self) -> None:
self._launched = True
self.set_world_ranks()
rank_zero_only.rank = self.global_rank
def set_world_ranks(self) -> None:
if self.cluster_environment is None:
return
rank_zero_only.rank = self.cluster_environment.global_rank()
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
assert self.model is not None
with self.precision_plugin.val_step_context():
return self.model(*args, **kwargs)
[docs] def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
assert self.model is not None
with self.precision_plugin.test_step_context():
return self.model(*args, **kwargs)
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
assert self.model is not None
with self.precision_plugin.predict_step_context():
return self.model(*args, **kwargs)
[docs] def on_train_batch_start(self, batch: Any, batch_idx: int) -> None:
self._pod_progress_bar_force_stdout()
[docs] def save_checkpoint(
self, checkpoint: Dict[str, Any], filepath: _PATH, storage_options: Optional[Any] = None
) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: dict containing model and trainer state
filepath: write-target file's path
storage_options: parameter for how to save to storage, passed to ``CheckpointIO`` plugin
"""
# `xla_model.save` needs to be called on all ranks. It internally checks if the local rank is 0
self.checkpoint_io.save_checkpoint(checkpoint, filepath, storage_options=storage_options)
[docs] def remove_checkpoint(self, filepath: _PATH) -> None:
"""Remove checkpoint filepath from the filesystem.
Args:
filepath: Path to checkpoint
"""
if self.local_rank == 0:
self.checkpoint_io.remove_checkpoint(filepath)
[docs] 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 of shape (batch, ...)
group: not available with TPUs
sync_grads: flag that allows users to synchronize gradients for the all_gather operation
Return:
A tensor of shape (world_size, batch, ...)
"""
if isinstance(tensor, Tensor) and tensor.dim() == 0:
tensor = tensor.unsqueeze(0)
import torch_xla.core.functions as xf
import torch_xla.core.xla_model as xm
return xf.all_gather(tensor) if sync_grads else xm.all_gather(tensor)
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register("xla_debug", cls, description="XLA strategy with `debug` as True", debug=True)
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
def _pod_progress_bar_force_stdout(self) -> None:
# Why is it required? The way `pytorch_xla.distributed` streams logs
# from different vms to the main worker doesn't work well with tqdm
# Ref: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140
# The print statement seems to force tqdm to flush stdout.
import torch_xla.core.xla_env_vars as xenv
if self.global_rank == 0 and int(os.getenv(xenv.TPUVM_MODE, 0)) == 1:
print()
