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Source code for pytorch_lightning.strategies.tpu_spawn

# Copyright The PyTorch Lightning 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, Union

import torch
from torch.nn import Module
from torch.utils.data import DataLoader

import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
from pytorch_lightning.strategies.launchers.xla_spawn import _XLASpawnLauncher
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, set_shared_parameters
from pytorch_lightning.utilities.data import has_len
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from pytorch_lightning.utilities.seed import reset_seed
from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT

if _TPU_AVAILABLE:
    import torch_xla.core.xla_env_vars as xenv
    import torch_xla.core.xla_model as xm
    import torch_xla.distributed.xla_multiprocessing as xmp
    from torch_xla.core.xla_model import rendezvous
    from torch_xla.distributed.parallel_loader import MpDeviceLoader
else:
    xm, xmp, MpDeviceLoader, rendezvous = [None] * 4


[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.Accelerator"] = None, parallel_devices: Optional[List[int]] = None, checkpoint_io: Optional[XLACheckpointIO] = None, precision_plugin: Optional[PrecisionPlugin] = None, debug: bool = False, **_: Any, ) -> None: checkpoint_io = checkpoint_io or XLACheckpointIO() super().__init__( accelerator=accelerator, parallel_devices=parallel_devices, checkpoint_io=checkpoint_io, precision_plugin=precision_plugin, ) self.debug = debug self.tpu_local_core_rank = 0 self.tpu_global_core_rank = 0 self.start_method = "fork" @property def global_rank(self) -> int: return self.tpu_global_core_rank @property def local_rank(self) -> int: return self.tpu_local_core_rank @property def world_size(self) -> int: return xm.xrt_world_size() @property def root_device(self) -> torch.device: return xm.xla_device() @staticmethod def _validate_dataloader(dataloaders: Union[List[DataLoader], DataLoader]) -> None: if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: 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." ) @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(): TPUSpawnStrategy._validate_dataloader(source.instance)
[docs] def connect(self, model: "pl.LightningModule") -> None: TPUSpawnStrategy._validate_patched_dataloaders(model) self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(model)) return super().connect(model)
def _configure_launcher(self): self._launcher = _XLASpawnLauncher(self)
[docs] def setup(self, trainer: "pl.Trainer") -> None: self.start_method = "fork" self.accelerator.setup(trainer) if self.debug: os.environ["PT_XLA_DEBUG"] = str(1) shared_params = find_shared_parameters(self.model) self.model_to_device() if is_overridden("on_post_move_to_device", self.lightning_module): self.model.module.on_post_move_to_device() else: set_shared_parameters(self.model.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: return model @property def distributed_sampler_kwargs(self) -> Dict[str, int]: return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()) @property def is_distributed(self) -> bool: # HOST_WORLD_SIZE is None outside the xmp.spawn process return os.getenv(xenv.HOST_WORLD_SIZE, None) and self.world_size != 1
[docs] def process_dataloader(self, dataloader: DataLoader) -> MpDeviceLoader: TPUSpawnStrategy._validate_dataloader(dataloader) dataloader = MpDeviceLoader(dataloader, self.root_device) # Mimic interface to torch.utils.data.DataLoader dataloader.dataset = dataloader._loader.dataset return dataloader
def configure_ddp(self) -> None: pass def init_dist_connection(self, global_rank: int, world_size: int) -> None: pass def set_world_ranks(self, process_idx: int = 0) -> None: pass
[docs] def model_to_device(self) -> None: self.model = self.wrapped_model.to(self.root_device)
[docs] def barrier(self, name: Optional[str] = None) -> None: if self.is_distributed: rendezvous(name)
[docs] def broadcast(self, obj: object, src: int = 0) -> object: 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) data = xm.all_gather(data_tensor) buffer = io.BytesIO(data.cpu().byte().numpy()) obj = torch.load(buffer) return obj
[docs] def reduce_boolean_decision(self, decision: bool) -> bool: decision = torch.tensor(int(decision), device=self.root_device) decision = self.reduce(decision, reduce_op="sum") decision = bool(decision == self.world_size) return decision
[docs] def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None): if not isinstance(output, torch.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 MisconfigurationException( "Currently, TPUSpawn Strategy only support `sum`, `mean`, `avg` reduce operation." ) 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 _worker_setup(self, process_idx: int): reset_seed() self.tpu_local_core_rank = xm.get_local_ordinal() self.tpu_global_core_rank = xm.get_ordinal() rank_zero_only.rank = self.global_rank
[docs] def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]: with self.precision_plugin.val_step_context(): return self.model(*args, **kwargs)
[docs] def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]: with self.precision_plugin.test_step_context(): return self.model(*args, **kwargs)
[docs] def predict_step(self, *args, **kwargs) -> STEP_OUTPUT: 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: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140 # The print statement seems to force tqdm to flush stdout. if self.tpu_global_core_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 """ # `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: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor: """ Function to gather a tensor from several distributed processes Args: tensor: tensor of shape (batch, ...) group: not available with TPUs sync_grads: not available with TPUs Return: A tensor of shape (world_size, batch, ...) """ if isinstance(tensor, torch.Tensor) and tensor.dim() == 0: tensor = tensor.unsqueeze(0) return 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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