Native Node Sleep/Wake Functionality for Neural Network Layers

[MikeyBeez]

🚀 The feature, motivation and pitch

Feature Request: Native Node Sleep/Wake Functionality for Neural Network Layers

Overview

This feature request proposes adding native functionality to temporarily deactivate ("sleep") and reactivate ("wake") specific nodes and weights within neural network layers. This capability would enable important research directions and training optimizations that are currently difficult to implement cleanly.

Motivation

Current approaches to modifying network architecture during training (such as pruning or freezing) are relatively crude - we either permanently remove connections or freeze entire layers. A more granular and reversible approach would enable new research directions and potentially more efficient training methods.

Key Benefits

1. Preservation of Potentially Important Pathways

• Instead of permanent pruning of low-weight connections, nodes could be temporarily deactivated
• Allows for reactivation when exploring new levels of abstraction or capabilities
• Particularly important for continuous learning systems where initially "unimportant" connections might become crucial later
• Enables empirical testing of theories about the role of weak connections in network development

2. Training with Selective Weight Freezing

• Freeze specific pathways while training others
• Allow new capacity to develop without disrupting existing knowledge
• Test approaches to preventing catastrophic forgetting
• Study how networks develop when different components are frozen/active at different times
• Enable more sophisticated approaches to transfer learning

3. Dynamic Architecture Optimization

• More flexible than current pruning approaches
• Enables experimentation with dynamic network growth and pruning
• Allows for temporary deactivation of pathways to study network behavior
• Support for adaptive architecture during training

4. Research Applications

• Study emergence of hierarchical representations
• Investigate network redundancy and pathway importance
• Examine how different parts of networks contribute to various abstraction levels
• Test hypotheses about neural network development inspired by biological systems
• Explore new approaches to architecture search

5. Training Optimization

• Selective activation/deactivation during different training phases
• Resource optimization without permanent architecture changes
• More granular control over network capacity
• Potential for more efficient training regimes

Current Limitations

The current approaches (using masks or requires_grad flags) are hacky and don't provide clean, efficient implementation of this functionality. These workarounds:

• Are often computationally inefficient
• Don't cleanly integrate with optimizers
• Can be error-prone
• Make experiments harder to implement and reproduce
• Don't properly handle all edge cases

Proposed API

# Layer-level functionality
class SleepableLayer(nn.Module):
    def sleep_nodes(self, indices):
        """Deactivate specific nodes"""
        pass
        
    def wake_nodes(self, indices):
        """Reactivate specific nodes"""
        pass
        
    def is_sleeping(self, indices):
        """Check sleep status of nodes"""
        pass
        
    def sleep_weights(self, indices):
        """Deactivate specific weights"""
        pass
        
    def wake_weights(self, indices):
        """Reactivate specific weights"""
        pass
        
    def get_sleep_state(self):
        """Return current sleep/wake configuration"""
        pass

# Model-level convenience functions
model.sleep_nodes(layer_name, indices)
model.wake_nodes(layer_name, indices)
model.get_sleep_configuration()

Implementation Considerations

Core Requirements

1. Efficient switching between active/inactive states
2. Proper gradient handling during backpropagation
3. Seamless integration with existing optimizers
4. Support for both node-level and weight-level control
5. Options for different levels of deactivation (full sleep vs weight freezing)
6. State preservation during save/load operations
7. Proper handling of batch normalization and other layer types
8. Clear documentation of behavior with different optimizer types

Performance Considerations

• Minimal memory overhead for sleep state tracking
• Efficient computation path for inactive nodes
• Batch operation support for sleep/wake operations
• Proper GPU memory management

Safety Features

• Validation of sleep/wake operations
• Warning for potentially problematic configurations
• State consistency checks
• Clear error messages for invalid operations

Benefits to the PyTorch Community

This functionality would:

1. Enable new research directions in network architecture and training
2. Make experiments more reproducible through standardized implementation
3. Reduce code complexity for many common training scenarios
4. Support innovation in network architecture research
5. Provide tools for studying network behavior and development

Submission Information

Primary Channels

1. GitHub Issue:
   Create new issue at https://github.com/pytorch/pytorch/issues
   Use label: "feature request"

2. PyTorch Discussion Forums:
   Post in "Feature Requests & Ideas" category at https://discuss.pytorch.org/

Additional Contacts

• PyTorch Developer Relations: dev-support@pytorch.org
• PyTorch Core Team (through GitHub)

Additional Resources

• Relevant research papers on network pruning and architecture
• Examples of current workarounds and their limitations
• Use cases from the research community
• Related issues and discussions in the PyTorch repository

Alternatives

No response

Additional context

No response

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki