lightning.pytorch.plugins.layer_sync — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

Source code for lightning.pytorch.plugins.layer_sync

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you may not use this file except in compliance with the License.

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from abc import ABC, abstractmethod

import torch from torch import Tensor from torch.nn import Module from typing_extensions import override

[docs]class LayerSync(ABC): """Abstract base class for creating plugins that wrap layers of a model with synchronization logic for multiprocessing."""

[docs] @abstractmethod def apply(self, model: Module) -> Module: """Override this method to apply synchronization to the layers of this model."""

[docs] @abstractmethod def revert(self, model: Module) -> Module: """Override this method to undo all modifications made in :meth:apply."""

[docs]class TorchSyncBatchNorm(LayerSync): """A plugin that wraps all batch normalization layers of a model with synchronization logic for multiprocessing.

This plugin has no effect in single-device operation.

"""

[docs] @override def apply(self, model: Module) -> Module: """Add global batchnorm for a model spread across multiple GPUs and nodes.

    Override this method to synchronize batchnorm layers between specific process groups instead
    of the whole world.

    Args:
        model: Reference to the current LightningModule

    Return:
        LightningModule with batchnorm layers synchronized within the process groups.

    """
    return torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

[docs] @override def revert(self, model: Module) -> Module: """Convert the wrapped batchnorm layers back to regular batchnorm layers.

    Args:
        model: Reference to the current LightningModule

    Return:
        LightningModule with regular batchnorm layers that will no longer sync across processes.

    """
    # Code adapted from https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547
    # Original author: Kapil Yedidi (@kapily)
    converted_module = model
    if isinstance(model, torch.nn.modules.batchnorm.SyncBatchNorm):
        # Unfortunately, LayerSync does not store the original class - if it did
        # we could return the one that was originally created.
        converted_module = _BatchNormXd(
            model.num_features, model.eps, model.momentum, model.affine, model.track_running_stats
        )
        if model.affine:
            with torch.no_grad():
                converted_module.weight = model.weight
                converted_module.bias = model.bias
        converted_module.running_mean = model.running_mean
        converted_module.running_var = model.running_var
        converted_module.num_batches_tracked = model.num_batches_tracked
        if hasattr(model, "qconfig"):
            converted_module.qconfig = model.qconfig
    for name, child in model.named_children():
        converted_module.add_module(name, self.revert(child))
    del model
    return converted_module

class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): @override def _check_input_dim(self, input: Tensor) -> None: # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc # is this method that is overwritten by the subclass. # Here, we are bypassing some tensor sanity checks and trusting that the user # provides the right input dimensions at inference. return