docs(Story-7): Add JIT Pattern Documentation for 76 Layer Rollout

[ooples]

Story 7: Pattern Documentation

Created comprehensive documentation to enable JIT compilation rollout across all 76 remaining neural network layers.

Documents Created

1. JIT_COMPILATION_PATTERN_GUIDE.md - Complete implementation guide

• Overview of JIT compilation in AiDotNet
• Performance benefits (5-10x speedup target)
• When to use JIT compilation
• Step-by-step implementation guide with complete code examples
• Common patterns: matrix operations, element-wise ops, convolution, pooling, normalization, attention
• Troubleshooting section with solutions to common issues
• Complete ConvolutionalLayer example

2. JIT_ACTIVATION_MAPPING.md - Activation support reference

• Table of all 37 activation functions
• 10 production-ready activations (ReLU, Sigmoid, Tanh, GELU, ELU, Mish, Swish, SiLU, LeakyReLU, Softmax, Identity)
• 27 available activations pending integration (SELU, CELU, PReLU, etc.)
• Integration examples for each activation
• Activation selection guide by model type (CNNs, Transformers, RNNs, GANs)
• IEngine integration status

3. JIT_ROADMAP.md - Current status and implementation roadmap

• Phase 1-2 completion summary (foundation + DenseLayer)
• Priority-ordered layer implementation list (6 priority levels)
• 76 layers categorized by importance and complexity
• Timeline estimates (2.5-10 months for full rollout)
• Batch implementation strategy
• Acceptance criteria for production-ready layers
• Future work: gradient computation, optimizations, extended activation support

Impact

Developers can now implement JIT compilation for:

• Priority 1 (Core): ConvolutionalLayer, LayerNormalizationLayer, PoolingLayer, BatchNormalizationLayer, DropoutLayer, FlattenLayer
• Priority 2 (RNN): LSTMLayer, GRULayer, RecurrentLayer
• Priority 3 (Attention): MultiHeadAttentionLayer, SelfAttentionLayer, AttentionLayer, TransformerEncoderLayer
• Priority 4-8: 64 additional specialized layers

Pattern Established

The documentation demonstrates the proven DenseLayer pattern:

• ExportComputationGraph with symbolic batch dimensions (-1)
• ApplyActivationToGraph helper method
• CanActivationBeJitted validation
• SupportsJitCompilation property
• Complete error handling and validation

Documentation Quality

• Total lines: 1,551 lines of comprehensive documentation
• Code examples: 15+ complete implementation examples
• Activations documented: 37 (10 ready, 27 pending)
• Layers prioritized: 76 with complexity estimates
• Patterns covered: 7 common computation patterns

Reference Implementation

All examples use DenseLayer from commit ec76111f as the reference implementation.

Next Steps

With this documentation, the community can:

1. Implement JIT support for Priority 1 layers (ConvolutionalLayer, etc.)
2. Follow the established pattern consistently
3. Extend activation support by adding to ApplyActivationToGraph
4. Track progress using the roadmap

🤖 Generated with Claude Code

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Summary by CodeRabbit

• Documentation
  • Added comprehensive guides covering JIT compilation support, including activation mappings, implementation patterns, and development roadmap for neural network layer optimization.

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Walkthrough

Three new documentation files added to guide JIT compilation implementation: an activation mapping reference categorizing 10 production-ready and 27 pending activations, a detailed implementation pattern guide with code examples and troubleshooting, and a phased rollout roadmap with layer priorities and timeline estimates.

Changes

Cohort / File(s)	Change Summary
JIT Compilation Documentation docs/JIT_ACTIVATION_MAPPING.md, docs/JIT_COMPILATION_PATTERN_GUIDE.md, docs/JIT_ROADMAP.md	Added three comprehensive guides: activation mapping reference with production-ready and pending status tables, implementation blueprint with step-by-step patterns and troubleshooting, and phased rollout roadmap with layer priorities and timeline estimates

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

• Verify code pattern accuracy in JIT_COMPILATION_PATTERN_GUIDE (ExportComputationGraph, ApplyActivationToGraph implementations)
• Ensure consistency of activation status and integration criteria across ACTIVATION_MAPPING and COMPILATION_PATTERN_GUIDE
• Validate timeline feasibility and layer priority ordering in JIT_ROADMAP align with actual implementation complexity

Poem

🐰 Three scrolls of wisdom, freshly penned,
JIT patterns now extend!
Activations mapped, roadmap clear,
From mapping to the finish line we steer,
Progress documented, guides prepared true! ✨

Pre-merge checks and finishing touches

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately describes the main change: comprehensive JIT pattern documentation for layer rollout.
Description check	✅ Passed	The description is well-structured and directly related to the changeset, detailing three new documentation files and their content.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing touches

🧪 Generate unit tests (beta)

• [ ] Create PR with unit tests
• [ ] Post copyable unit tests in a comment
• [ ] Commit unit tests in branch feat/jit-pattern-documentation

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