rUv
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d5b3be56b8
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feat(decompiler): ONNX Runtime neural inference + 8,226 training pairs
Neural inference (behind `neural` feature flag):
- Full ONNX Runtime integration via `ort` crate
- Loads .onnx models, encodes context as byte tensors
- Softmax confidence scoring, character-level decoding
- Falls back to pattern-based when model unavailable
Training data expansion: 1,602 → 8,226 pairs
- 200+ function names, 90+ class names, 170+ variable names
- 16 minifier styles, 5 context variations per entry
- Extracted identifier dictionaries (381 lines)
Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-04-03 02:30:41 +00:00 |
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rUv
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84e1886451
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feat(decompiler): GPU training pipeline for neural name inference (ADR-136)
Training pipeline:
- generate-deobfuscation-data.mjs: 1,200+ training pairs from fixtures + synthetic
- train-deobfuscator.py: 6M param transformer (3 layers, 4 heads, 128 embed)
- export-to-rvf.py: PyTorch → ONNX → GGUF Q4 → RVF OVERLAY
- launch-gpu-training.sh: GCloud L4 GPU (--local, --cloud-run, --spot)
- Dockerfile.deobfuscator: pytorch/pytorch:2.2.0-cuda12.1
Decompiler integration:
- NeuralInferrer behind optional `neural` feature flag
- model_path in DecompileConfig
- Falls through to pattern-based when model unavailable
- Zero binary impact without feature flag
All tests pass, cargo check clean with and without neural feature.
Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-04-03 02:08:19 +00:00 |
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