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In the
TensorPlay framework, hooks provide a powerful mechanism for injecting custom logic into neural network operations. Implemented in the Layer class and its subclasses (including Module), hooks allow developers to monitor, debug, and modify computational processes during both forward and backward propagation without changing the core framework code.For easier reading: this article focuses on
TensorPlay’s hook mechanism. The sections follow this order: Types → Signatures → Management → Flow → Applications → Example.🧩 Hook types and registration
TensorPlay offers three distinct hook types, each activating at specific stages of network execution:⏭️ Forward pre hooks
- Registration:
register_forward_pre_hook(hook)
- Activation: Just before the
forwardmethod executes
- Purpose: Modify inputs or configure layer behavior prior to computation
📤 Forward hooks
- Registration:
register_forward_hook(hook)
- Activation: Immediately after the
forwardmethod completes
- Purpose: Capture or process output values from layer computations
🔁 Backward hooks
- Registration:
register_backward_hook(hook)
- Activation: During gradient propagation phase
- Purpose: Inspect or modify gradient values during backpropagation
✍️ Hook function signatures
Forward pre hook
Forward hook
Backward hook
🧷 Managing hooks
- Registration: Each registration method returns a handle for later reference
- Removal: Use the returned handle to remove hooks when no longer needed
🛠️ Execution flow integration
Hooks integrate seamlessly into the network's computation pipeline:
- Forward pre hooks execute first via
_call_forward_pre_hooks
- The layer
forwardmethod processes inputs
- Output tensors are linked to their source module for traceability
- Forward hooks run through
_call_forward_hooks
- Backward hooks activate during gradient propagation in
Operator.propagate_grad
📌 Practical applications
- Debugging: Inspect intermediate values and gradients to identify anomalies
- Feature visualization: Capture activation patterns from hidden layers
- Gradient manipulation: Implement gradient clipping to stabilize training
- Dynamic adjustment: Modify layer behavior based on computation results
- Performance profiling: Measure execution times to identify bottlenecks
- Logging: Record network behavior for analysis or reporting
🧪 Implementation example
This hook system provides flexibility, empowering researchers and developers to extend framework capabilities while maintaining a clean separation between core logic and custom functionality.
