pcDEQ Transformers. #3
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Pull Request Overview
This PR introduces a complete implementation of pcDEQ (Positively Constrained Deep Equilibrium) Transformers, which includes fixed-point solvers, neural network blocks, layers, and complete network architectures for different configurations.
Key changes:
- Implementation of fixed-point iteration solver with convergence monitoring
- Creation of pcDEQ blocks and layers for linear, convolutional, and transformer architectures
- Development of complete network classes supporting different pcDEQ variants
Reviewed Changes
Copilot reviewed 4 out of 5 changed files in this pull request and generated 5 comments.
| File | Description |
|---|---|
| src/gpt2/modeling/deq/solvers.py | Implements fixed-point iteration solver with error tracking and convergence criteria |
| src/gpt2/modeling/deq/networks.py | Provides complete network architectures for linear, convolutional, and transformer pcDEQ models |
| src/gpt2/modeling/deq/layers.py | Defines base and specialized pcDEQ layers with weight initialization and activation functions |
| src/gpt2/modeling/deq/blocks.py | Implements DEQ fixed-point blocks with custom gradient computation and weight clamping |
- Added type hints. - Using softsign activation - Clamp on `weight`, no weight norm used.
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