XLAStrategy

class lightning.pytorch.strategies.XLAStrategy(accelerator=None, parallel_devices=None, checkpoint_io=None, precision_plugin=None, debug=False, **_)

Bases: lightning.pytorch.strategies.ddp.DDPStrategy

Strategy for training multiple TPU devices using the torch_xla.distributed.xla_multiprocessing.spawn() method.

all_gather(tensor, group=None, sync_grads=False)

Function to gather a tensor from several distributed processes.

Parameters

• tensor (Tensor) – tensor of shape (batch, …)

• group (Optional[Any]) – not available with TPUs

• sync_grads (bool) – flag that allows users to synchronize gradients for the all_gather operation

Return type

Tensor

Returns

A tensor of shape (world_size, batch, …)

barrier(name=None, *args, **kwargs)

Synchronizes all processes which blocks processes until the whole group enters this function.

Parameters

name (Optional[str]) – an optional name to pass into barrier.

Return type

None

broadcast(obj, src=0)

Broadcasts an object to all processes.

Parameters

• obj (TypeVar(TBroadcast)) – the object to broadcast

• src (int) – source rank

Return type

TypeVar(TBroadcast)

connect(model)

Called by the accelerator to connect the accelerator and the model with this plugin.

Return type

None

model_to_device()

Moves the model to the correct device.

Return type

None

on_train_batch_start(batch, batch_idx)

Called in the training loop before anything happens for that batch.

Return type

None

predict_step(*args, **kwargs)

The actual predict step.

See predict_step() for more details

Return type

Union[Tensor, Dict[str, Any]]

process_dataloader(dataloader)

Wraps the dataloader if necessary.

Parameters

dataloader (object) – iterable. Ideally of type: torch.utils.data.DataLoader

Return type

None

reduce(output, group=None, reduce_op=None)

Reduces a tensor from several distributed processes to one aggregated tensor.

Parameters

• tensor – the tensor to sync and reduce

• group (Optional[Any]) – the process group to gather results from. Defaults to all processes (world)

• reduce_op (Union[ReduceOp, str, None]) – the reduction operation. Defaults to ‘mean’/’avg’. Can also be a string ‘sum’ to calculate the sum during reduction.

Return type

Tensor

Returns

reduced value, except when the input was not a tensor the output remains is unchanged

remove_checkpoint(filepath)

Remove checkpoint filepath from the filesystem.

Parameters

filepath (Union[str, Path]) – Path to checkpoint

Return type

None

save_checkpoint(checkpoint, filepath, storage_options=None)

Save model/training states as a checkpoint file through state-dump and file-write.

Parameters

• checkpoint (Dict[str, Any]) – dict containing model and trainer state

• filepath (Union[str, Path]) – write-target file’s path

• storage_options (Optional[Any]) – parameter for how to save to storage, passed to CheckpointIO plugin

Return type

None

setup(trainer)

Setup plugins for the trainer fit and creates optimizers.

Parameters

trainer (Trainer) – the trainer instance

Return type

None

teardown()

This method is called to teardown the training process.

It is the right place to release memory and free other resources.

Return type

None

test_step(*args, **kwargs)

The actual test step.

See test_step() for more details

Return type

Union[Tensor, Dict[str, Any], None]

validation_step(*args, **kwargs)

The actual validation step.

See validation_step() for more details

Return type

Union[Tensor, Dict[str, Any], None]

property root_device: torch.device

Return the root device.

Return type

device