🧱 Mason: Decouple Transformer Layers (Fixes DIP)

[fderuiter]

🧱 Mason: Decouple Transformer Layers (Fixes DIP)

🏚️ Violation: EncoderLayer and DecoderLayer were tightly coupled to concrete implementations of MultiHeadAttention, FeedForward, and LayerNorm, violating the Dependency Inversion Principle and making extensions (e.g., Sparse Attention) impossible without modification.

🏗️ Fix:

1. Introduced traits module with AttentionMechanism, FeedForwardNetwork, and NormalizationLayer.
2. Implemented these traits for MultiHeadAttention, FeedForward, and LayerNorm.
3. Refactored EncoderLayer and DecoderLayer to hold Box<dyn Trait> components.
4. Added new_with_components for dependency injection.

🔗 Principle: Dependency Inversion Principle (DIP) and Open/Closed Principle (OCP).

🧪 Verification:

• cargo check passed.
• cargo test passed (including ai::transformer tests).
• No changes to external behavior of new constructors.

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PR created automatically by Jules for task 7450992865170839797 started by @fderuiter

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