Source code for pytorch_lightning.strategies.tpu_spawn

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import os
from typing import Any, Dict, List, Mapping, Optional, Sequence, TYPE_CHECKING, Union

import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.nn import Module
from import DataLoader

import pytorch_lightning 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 import has_len
from lightning_fabric.utilities.optimizer import _optimizers_to_device
from lightning_fabric.utilities.types import _PATH, ReduceOp
from pytorch_lightning.overrides import LightningDistributedModule
from import _WrappingCheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
from pytorch_lightning.strategies.launchers.xla import _XLALauncher
from pytorch_lightning.strategies.strategy import TBroadcast
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities import find_shared_parameters, set_shared_parameters
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from pytorch_lightning.utilities.types import EVAL_DATALOADERS, STEP_OUTPUT, TRAIN_DATALOADERS

    from torch_xla.distributed.parallel_loader import MpDeviceLoader
    MpDeviceLoader = None

[docs]class TPUSpawnStrategy(DDPSpawnStrategy): """Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn` method.""" strategy_name = "tpu_spawn" 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(dataloaders: Union[TRAIN_DATALOADERS, EVAL_DATALOADERS]) -> None: def check_has_len(dataloader: DataLoader) -> None: if not has_len(dataloader): raise MisconfigurationException( "TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`." " HINT: You can mock the length on your dataset to bypass this MisconfigurationException." ) apply_to_collection(dataloaders, dtype=object, wrong_dtype=(Sequence, Mapping), function=check_has_len) @staticmethod def _validate_patched_dataloaders(model: "pl.LightningModule") -> None: """Validate and fail fast if the dataloaders were passed directly to fit.""" connector: DataConnector = model.trainer._data_connector sources = ( connector._train_dataloader_source, connector._val_dataloader_source, connector._test_dataloader_source, connector._predict_dataloader_source, ) for source in sources: if not source.is_module(): assert source.instance is not None assert not isinstance(source.instance, (pl.LightningModule, pl.LightningDataModule)) TPUSpawnStrategy._validate_dataloader(source.instance)
[docs] def connect(self, model: "pl.LightningModule") -> None: TPUSpawnStrategy._validate_patched_dataloaders(model) import torch_xla.distributed.xla_multiprocessing as xmp self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(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: DataLoader) -> "MpDeviceLoader": TPUSpawnStrategy._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 dataloader.dataset = dataloader._loader.dataset dataloader.batch_sampler = getattr(dataloader._loader, "batch_sampler", None) return dataloader
def configure_ddp(self) -> None: pass
[docs] def model_to_device(self) -> None: self.model =
[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(), 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 TPUSpawnStrategy 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)
def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT: self._pod_progress_bar_force_stdout() return output def validation_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT: self._pod_progress_bar_force_stdout() return output def test_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT: self._pod_progress_bar_force_stdout() return output 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: # 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()
[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 """ # `` 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)
[docs] def teardown(self) -> None: super().teardown() os.environ.pop("PT_XLA_DEBUG", None)
@classmethod def register_strategies(cls, strategy_registry: Dict) -> None: strategy_registry.register( "tpu_spawn_debug", cls, description="TPUSpawn Strategy with `debug` as True", debug=True ) strategy_registry.register( cls.strategy_name, cls, description=f"{cls.__class__.__name__}", )

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