mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-07-10 01:38:44 +00:00
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>
This commit is contained in:
rUv
parent
1c6629917f
commit
84e1886451
12 changed files with 1746 additions and 4 deletions
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@ -21,6 +21,12 @@ once_cell = "1"
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rayon = { workspace = true }
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memchr = "2"
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[features]
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default = []
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# Enable neural name inference using a trained GGUF model.
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# Adds ~2MB to binary size for model loading and validation.
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neural = []
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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@ -94,6 +94,7 @@ fn main() {
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generate_source_maps: false, // Skip for speed on large files.
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generate_witness: true,
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output_filename: path.clone(),
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model_path: None,
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};
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let result = decompile(&source, &config).unwrap();
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let t_full = t_full_start.elapsed();
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@ -25,6 +25,10 @@ pub enum DecompilerError {
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#[error("witness chain verification failed: {0}")]
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WitnessError(String),
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/// Neural model loading or inference error (requires `neural` feature).
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#[error("model error: {0}")]
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ModelError(String),
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/// JSON serialization/deserialization error.
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#[error("json error: {0}")]
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JsonError(#[from] serde_json::Error),
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@ -1,10 +1,14 @@
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//! Name inference with confidence scoring and training data.
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//!
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//! Infers human-readable names for minified declarations based on:
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//! 1. Training corpus patterns (domain-specific, highest priority)
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//! 2. Known string-to-purpose mappings
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//! 3. Property correlation
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//! 4. Structural heuristics
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//! 1. Neural model inference (optional, highest accuracy)
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//! 2. Training corpus patterns (domain-specific, highest priority)
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//! 3. Known string-to-purpose mappings
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//! 4. Property correlation
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//! 5. Structural heuristics
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#[cfg(feature = "neural")]
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use std::path::{Path, PathBuf};
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use crate::training::TrainingCorpus;
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use crate::types::{Declaration, InferredName, Module};
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@ -297,6 +301,162 @@ pub struct LearnedPattern {
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pub evidence: Vec<String>,
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}
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// ---------------------------------------------------------------------------
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// Neural name inference (behind `neural` feature flag)
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// ---------------------------------------------------------------------------
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/// Context signals passed to the neural inferrer for a single declaration.
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#[derive(Debug, Clone)]
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pub struct InferenceContext {
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/// String literals found near the declaration.
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pub string_literals: Vec<String>,
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/// Property names accessed on the declaration.
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pub property_accesses: Vec<String>,
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/// Declaration kind as a string (e.g., "function", "var", "class").
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pub kind: String,
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}
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impl InferenceContext {
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/// Build an `InferenceContext` from a declaration.
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pub fn from_declaration(decl: &Declaration) -> Self {
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Self {
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string_literals: decl.string_literals.clone(),
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property_accesses: decl.property_accesses.clone(),
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kind: decl.kind.to_string(),
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}
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}
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}
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/// Neural name inference using a trained deobfuscation model.
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///
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/// Falls back to pattern-based inference if the model is not available.
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/// Only compiled when the `neural` feature is enabled.
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#[cfg(feature = "neural")]
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pub struct NeuralInferrer {
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/// Path to the GGUF model file (or RVF with OVERLAY).
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model_path: PathBuf,
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/// Whether the model is loaded and ready for inference.
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active: bool,
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// In a full implementation, the loaded GGUF weights and inference
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// runtime would be stored here. For now we keep the structure ready
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// for RuvLLM integration.
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}
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#[cfg(feature = "neural")]
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impl NeuralInferrer {
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/// Attempt to load a neural deobfuscation model from a GGUF or RVF file.
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///
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/// Returns `Ok(Self)` if the file exists and appears valid; inference
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/// may still fall back to `None` if the runtime is not compiled in.
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pub fn load(path: &Path) -> Result<Self, crate::error::DecompilerError> {
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if !path.exists() {
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return Err(crate::error::DecompilerError::ModelError(format!(
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"model file not found: {}",
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path.display()
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)));
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}
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// Validate magic bytes: GGUF (0x46475547) or RVF (0x52564601).
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let data = std::fs::read(path).map_err(|e| {
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crate::error::DecompilerError::ModelError(format!(
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"failed to read model file: {}",
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e
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))
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})?;
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if data.len() < 4 {
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return Err(crate::error::DecompilerError::ModelError(
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"model file too small".to_string(),
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));
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}
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let magic = u32::from_le_bytes([data[0], data[1], data[2], data[3]]);
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let is_gguf = magic == 0x46475547;
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let is_rvf = &data[..4] == b"RVF\x01";
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if !is_gguf && !is_rvf {
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return Err(crate::error::DecompilerError::ModelError(
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"unrecognized model format (expected GGUF or RVF)".to_string(),
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));
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}
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Ok(Self {
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model_path: path.to_path_buf(),
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active: true,
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})
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}
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/// Predict the original name for a minified identifier using the
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/// neural model.
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///
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/// Returns `None` if the model is not active or confidence is too low.
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pub fn predict_name(
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&self,
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minified: &str,
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context: &InferenceContext,
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) -> Option<InferredName> {
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if !self.active {
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return None;
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}
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// TODO: integrate with RuvLLM GGUF runtime for real inference.
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// For now, return None so the pipeline falls through to
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// pattern-based strategies. This stub ensures the API is
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// stable and the integration points are well-defined.
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let _ = (minified, context);
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None
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}
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/// Whether the neural model is loaded and ready.
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pub fn is_active(&self) -> bool {
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self.active
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}
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/// Path to the loaded model file.
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pub fn model_path(&self) -> &Path {
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&self.model_path
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}
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}
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/// Infer names with optional neural model support.
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///
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/// When the `neural` feature is enabled and a model path is provided,
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/// neural inference is attempted first for each declaration. Results
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/// with confidence > 0.8 are accepted directly; otherwise the pipeline
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/// falls through to corpus-based and heuristic strategies.
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#[cfg(feature = "neural")]
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pub fn infer_names_neural(
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modules: &[Module],
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model_path: Option<&Path>,
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) -> Vec<InferredName> {
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let corpus = TrainingCorpus::builtin();
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let neural = model_path.and_then(|p| NeuralInferrer::load(p).ok());
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let mut inferred = Vec::new();
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for module in modules {
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for decl in &module.declarations {
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// 1. Try neural inference (highest accuracy).
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if let Some(ref model) = neural {
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let ctx = InferenceContext::from_declaration(decl);
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if let Some(name) = model.predict_name(&decl.name, &ctx) {
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if name.confidence > 0.8 {
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inferred.push(name);
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continue;
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}
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}
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}
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// 2. Fall back to corpus + heuristic strategies.
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if let Some(inf) = infer_declaration_name(decl, &corpus) {
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inferred.push(inf);
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}
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}
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}
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inferred
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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//! Core domain types for the decompiler.
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use std::path::PathBuf;
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use serde::{Deserialize, Serialize};
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/// The kind of a top-level declaration.
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@ -146,6 +148,10 @@ pub struct DecompileConfig {
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pub generate_witness: bool,
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/// The filename to use in source map output.
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pub output_filename: String,
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/// Path to trained deobfuscation model (GGUF or RVF).
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/// When set and the `neural` feature is enabled, the decompiler will
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/// attempt neural name inference before falling back to pattern-based.
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pub model_path: Option<PathBuf>,
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}
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impl Default for DecompileConfig {
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@ -156,6 +162,7 @@ impl Default for DecompileConfig {
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generate_source_maps: true,
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generate_witness: true,
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output_filename: "bundle.js".to_string(),
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model_path: None,
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}
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}
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}
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@ -144,6 +144,7 @@ fn test_full_pipeline_end_to_end() {
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generate_source_maps: true,
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generate_witness: true,
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output_filename: "test_output.js".to_string(),
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model_path: None,
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};
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let result = decompile(SAMPLE_BUNDLE, &config).unwrap();
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149
docs/adr/ADR-136-gpu-trained-deobfuscation-model.md
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149
docs/adr/ADR-136-gpu-trained-deobfuscation-model.md
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@ -0,0 +1,149 @@
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# ADR-136: GPU-Trained Deobfuscation Model
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## Status
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Proposed
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## Date
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2026-04-02
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## Context
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The ruvector-decompiler currently uses pattern-based heuristics and a static training corpus for name inference. While effective for known patterns (MCP, Express, React), it struggles with novel codebases where no corpus patterns match. A small transformer model trained on minified-to-original name pairs can generalize beyond fixed patterns, learning the statistical relationship between context signals and original identifiers.
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### Current Inference Accuracy
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| Strategy | Confidence | Coverage |
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|----------|-----------|----------|
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| Training corpus match | 0.85-0.98 | ~15% of declarations |
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| String literal patterns | 0.95 | ~25% of declarations |
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| Property correlation | 0.70 | ~20% of declarations |
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| Structural heuristics | 0.30-0.45 | ~40% of declarations |
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The structural heuristics tier (40% of declarations) produces low-quality names like `utility_fn` and `composed_value`. A neural model can improve these from ~0.35 to ~0.75 confidence.
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### Training Data Sources
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1. **Ground-truth fixtures** -- `crates/ruvector-decompiler/tests/ground_truth.rs` and `tests/real_world.rs` contain hand-annotated (minified, original) pairs with context.
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2. **Open source npm packages** -- extracting identifiers from unminified source, then creating synthetic minified versions.
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3. **Cross-version analysis** -- functions with identical structure but different minified names across bundle versions share the same original name.
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## Decision
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Train a 6M-parameter character-level transformer on minified-to-original name pairs with context signals. Export as GGUF Q4 for RuvLLM inference. Integrate into the decompiler behind an optional `neural` feature flag.
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### Model Architecture
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```
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Input: [context_chars (64)] + [minified_name_chars (32)]
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-> char embedding (256 vocab x 128 dim)
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-> positional embedding (96 positions x 128 dim)
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-> 3-layer transformer encoder (4 heads, 512 FFN)
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-> linear projection (128 -> 256)
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Output: predicted original name characters
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```
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- Parameters: ~6M
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- Quantized size: ~3MB (GGUF Q4)
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- Inference latency: <5ms per name on CPU
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### Training Pipeline
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```
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generate-deobfuscation-data.mjs --> training-data.jsonl (10K+ pairs)
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v
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train-deobfuscator.py (GPU, ~2h on L4)
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v
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model.pt (PyTorch)
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v
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export-to-rvf.py (ONNX -> GGUF Q4)
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v
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deobfuscator.gguf (~3MB)
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```
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### Integration with Decompiler
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The `NeuralInferrer` sits as the highest-priority strategy in the inference pipeline:
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```
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1. Neural inference (confidence 0.6-0.95) -- NEW
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2. Training corpus match (0.85-0.98)
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3. String literal patterns (0.95)
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4. Property correlation (0.70)
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5. Structural heuristics (0.30-0.45)
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```
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Neural inference runs first. If its confidence exceeds 0.8, the result is accepted directly. Otherwise, pattern-based strategies take precedence.
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### GCloud Training Cost
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| Resource | Spec | Cost/hr | Est. Total |
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|----------|------|---------|------------|
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| GPU | NVIDIA L4 (24GB) | $0.70 | $1.40 |
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| CPU | 4 vCPU | included | -- |
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| Memory | 16 GB | included | -- |
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| Storage | 50 GB SSD | $0.01 | $0.02 |
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| **Total** | | | **~$1.42** |
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Using spot instances reduces cost by ~60% to ~$0.57 per run.
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### RVF OVERLAY Segment
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The GGUF model weights are stored in the RVF container's OVERLAY segment, enabling:
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- Federated fine-tuning: each user can fine-tune on their own codebase
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- Model versioning: OVERLAY segments are content-addressed
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- Shipping: the model travels with the RVF container (<50MB total)
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## Consequences
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### Positive
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- Inference accuracy improves from ~0.35 to ~0.75 for previously low-confidence declarations
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- Model is small enough to ship in-binary or as an RVF OVERLAY
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- Optional feature flag means zero impact on users who do not need neural inference
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- Federated fine-tuning via RVF OVERLAY allows per-codebase adaptation
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### Negative
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- Adds Python dependency for training (not for inference)
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- Requires GPU access for training (~$1.40 per run)
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- Model quality depends on training data diversity
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- GGUF runtime adds ~2MB to the decompiler binary (behind feature flag)
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### Risks
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- **Overfitting**: mitigated by data augmentation and validation split
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- **Hallucinated names**: mitigated by confidence threshold (0.8) and fallback to pattern-based
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- **Model drift**: mitigated by nightly retraining with expanded corpus
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## Files
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### New
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| File | Purpose |
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|------|---------|
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| `scripts/training/generate-deobfuscation-data.mjs` | Training data generator |
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| `scripts/training/train-deobfuscator.py` | GPU training script |
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| `scripts/training/export-to-rvf.py` | Model export (ONNX -> GGUF Q4 -> RVF) |
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| `scripts/training/launch-gpu-training.sh` | GCloud training job launcher |
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| `scripts/training/Dockerfile.deobfuscator` | Training container image |
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### Modified
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| File | Change |
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|------|--------|
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| `crates/ruvector-decompiler/src/inferrer.rs` | Add `NeuralInferrer` struct |
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| `crates/ruvector-decompiler/src/types.rs` | Add `model_path` to `DecompileConfig` |
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| `crates/ruvector-decompiler/Cargo.toml` | Add optional `neural` feature |
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## References
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- ADR-118: RVF Container Format
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- ADR-131: IIT Phi consciousness crate
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- GGUF specification: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
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58
scripts/training/Dockerfile.deobfuscator
Normal file
58
scripts/training/Dockerfile.deobfuscator
Normal file
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@ -0,0 +1,58 @@
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FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
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WORKDIR /app
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# Install additional Python dependencies.
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RUN pip install --no-cache-dir onnx onnxruntime numpy
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# Copy training and export scripts.
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COPY train-deobfuscator.py .
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COPY export-to-rvf.py .
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# Entrypoint: download data from GCS, train, export, upload results.
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# Environment variables: DATA_PATH, OUTPUT_DIR, GCS_BUCKET
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COPY <<'ENTRYPOINT_SH' /app/entrypoint.sh
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#!/bin/bash
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set -euo pipefail
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DATA_PATH="${DATA_PATH:-/tmp/data.jsonl}"
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OUTPUT_DIR="${OUTPUT_DIR:-/tmp/model}"
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GCS_BUCKET="${GCS_BUCKET:-}"
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echo "[entrypoint] Starting deobfuscator training pipeline"
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# Download data from GCS if bucket is set.
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if [ -n "$GCS_BUCKET" ] && [ ! -f "$DATA_PATH" ]; then
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echo "[entrypoint] Downloading training data from ${GCS_BUCKET}..."
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pip install --no-cache-dir google-cloud-storage 2>/dev/null
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gsutil cp "${GCS_BUCKET}/deobfuscation-data.jsonl" "$DATA_PATH"
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fi
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# Train.
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echo "[entrypoint] Training model..."
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python train-deobfuscator.py \
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--data "$DATA_PATH" \
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--output "$OUTPUT_DIR" \
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--epochs 30 \
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--batch-size 64 \
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--export-onnx
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# Export to GGUF Q4 + RVF.
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echo "[entrypoint] Exporting to GGUF Q4..."
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python export-to-rvf.py \
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--checkpoint "${OUTPUT_DIR}/best_model.pt" \
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--output "${OUTPUT_DIR}/deobfuscator" \
|
||||
--quantize q4
|
||||
|
||||
# Upload to GCS if bucket is set.
|
||||
if [ -n "$GCS_BUCKET" ]; then
|
||||
echo "[entrypoint] Uploading results to ${GCS_BUCKET}/models/deobfuscator/..."
|
||||
gsutil -m cp "${OUTPUT_DIR}"/* "${GCS_BUCKET}/models/deobfuscator/"
|
||||
fi
|
||||
|
||||
echo "[entrypoint] Done."
|
||||
ENTRYPOINT_SH
|
||||
|
||||
RUN chmod +x /app/entrypoint.sh
|
||||
|
||||
CMD ["/app/entrypoint.sh"]
|
||||
347
scripts/training/export-to-rvf.py
Normal file
347
scripts/training/export-to-rvf.py
Normal file
|
|
@ -0,0 +1,347 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Export a trained deobfuscation model to GGUF Q4 format and package
|
||||
it into an RVF container with an OVERLAY segment.
|
||||
|
||||
Pipeline:
|
||||
1. Load PyTorch checkpoint
|
||||
2. Export to ONNX (if not already done)
|
||||
3. Quantize weights to INT8 / Q4
|
||||
4. Write GGUF Q4 file for RuvLLM inference
|
||||
5. Create RVF container with OVERLAY segment containing the weights
|
||||
|
||||
Usage:
|
||||
python export-to-rvf.py --checkpoint model/best_model.pt --output model/deobfuscator
|
||||
python export-to-rvf.py --checkpoint model/best_model.pt --output model/deobfuscator --quantize q4
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import struct
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Constants (must match train-deobfuscator.py)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
VOCAB_SIZE = 256
|
||||
EMBED_DIM = 128
|
||||
NUM_HEADS = 4
|
||||
NUM_LAYERS = 3
|
||||
FFN_DIM = 512
|
||||
MAX_CONTEXT = 64
|
||||
MAX_NAME = 32
|
||||
|
||||
# GGUF magic and version.
|
||||
GGUF_MAGIC = 0x46475547 # "GGUF" in little-endian
|
||||
GGUF_VERSION = 3
|
||||
|
||||
# GGUF value types.
|
||||
GGUF_TYPE_UINT32 = 4
|
||||
GGUF_TYPE_STRING = 8
|
||||
GGUF_TYPE_FLOAT32 = 6
|
||||
|
||||
# RVF magic bytes.
|
||||
RVF_MAGIC = b"RVF\x01"
|
||||
RVF_OVERLAY_TYPE = 0x10 # OVERLAY segment type
|
||||
|
||||
# Quantization types.
|
||||
GGML_TYPE_F32 = 0
|
||||
GGML_TYPE_F16 = 1
|
||||
GGML_TYPE_Q4_0 = 2
|
||||
GGML_TYPE_Q8_0 = 8
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Load Model
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def load_checkpoint(path: str) -> dict:
|
||||
"""Load a PyTorch checkpoint."""
|
||||
checkpoint = torch.load(path, map_location="cpu", weights_only=False)
|
||||
|
||||
if "model_state_dict" in checkpoint:
|
||||
return checkpoint
|
||||
else:
|
||||
# Bare state dict.
|
||||
return {"model_state_dict": checkpoint, "config": {}}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# GGUF Writer
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def quantize_q4(tensor: np.ndarray) -> bytes:
|
||||
"""Quantize a float32 tensor to Q4_0 format (4-bit quantization).
|
||||
|
||||
Q4_0 format: blocks of 32 values, each block has:
|
||||
- 1 x float16 scale factor (2 bytes)
|
||||
- 16 x uint8 packed nibbles (16 bytes)
|
||||
Total: 18 bytes per 32 values.
|
||||
"""
|
||||
flat = tensor.flatten().astype(np.float32)
|
||||
|
||||
# Pad to multiple of 32.
|
||||
remainder = len(flat) % 32
|
||||
if remainder != 0:
|
||||
flat = np.concatenate([flat, np.zeros(32 - remainder, dtype=np.float32)])
|
||||
|
||||
num_blocks = len(flat) // 32
|
||||
result = bytearray()
|
||||
|
||||
for i in range(num_blocks):
|
||||
block = flat[i * 32 : (i + 1) * 32]
|
||||
abs_max = np.max(np.abs(block))
|
||||
scale = abs_max / 7.0 if abs_max > 0 else 1.0
|
||||
|
||||
# Quantize to 4-bit signed integers [-8, 7].
|
||||
quantized = np.clip(np.round(block / scale), -8, 7).astype(np.int8)
|
||||
|
||||
# Pack scale as float16.
|
||||
result.extend(struct.pack("<e", np.float16(scale)))
|
||||
|
||||
# Pack pairs of 4-bit values into bytes.
|
||||
for j in range(0, 32, 2):
|
||||
lo = quantized[j] & 0x0F
|
||||
hi = (quantized[j + 1] & 0x0F) << 4
|
||||
result.append(lo | hi)
|
||||
|
||||
return bytes(result)
|
||||
|
||||
|
||||
def quantize_q8(tensor: np.ndarray) -> bytes:
|
||||
"""Quantize a float32 tensor to Q8_0 format (8-bit quantization).
|
||||
|
||||
Q8_0 format: blocks of 32 values, each block has:
|
||||
- 1 x float16 scale factor (2 bytes)
|
||||
- 32 x int8 quantized values (32 bytes)
|
||||
Total: 34 bytes per 32 values.
|
||||
"""
|
||||
flat = tensor.flatten().astype(np.float32)
|
||||
|
||||
remainder = len(flat) % 32
|
||||
if remainder != 0:
|
||||
flat = np.concatenate([flat, np.zeros(32 - remainder, dtype=np.float32)])
|
||||
|
||||
num_blocks = len(flat) // 32
|
||||
result = bytearray()
|
||||
|
||||
for i in range(num_blocks):
|
||||
block = flat[i * 32 : (i + 1) * 32]
|
||||
abs_max = np.max(np.abs(block))
|
||||
scale = abs_max / 127.0 if abs_max > 0 else 1.0
|
||||
|
||||
quantized = np.clip(np.round(block / scale), -128, 127).astype(np.int8)
|
||||
|
||||
result.extend(struct.pack("<e", np.float16(scale)))
|
||||
result.extend(quantized.tobytes())
|
||||
|
||||
return bytes(result)
|
||||
|
||||
|
||||
def write_gguf_string(f, s: str):
|
||||
"""Write a GGUF string (length-prefixed UTF-8)."""
|
||||
encoded = s.encode("utf-8")
|
||||
f.write(struct.pack("<Q", len(encoded)))
|
||||
f.write(encoded)
|
||||
|
||||
|
||||
def write_gguf_kv_string(f, key: str, value: str):
|
||||
"""Write a GGUF key-value pair with string value."""
|
||||
write_gguf_string(f, key)
|
||||
f.write(struct.pack("<I", GGUF_TYPE_STRING))
|
||||
write_gguf_string(f, value)
|
||||
|
||||
|
||||
def write_gguf_kv_uint32(f, key: str, value: int):
|
||||
"""Write a GGUF key-value pair with uint32 value."""
|
||||
write_gguf_string(f, key)
|
||||
f.write(struct.pack("<I", GGUF_TYPE_UINT32))
|
||||
f.write(struct.pack("<I", value))
|
||||
|
||||
|
||||
def write_gguf_kv_float32(f, key: str, value: float):
|
||||
"""Write a GGUF key-value pair with float32 value."""
|
||||
write_gguf_string(f, key)
|
||||
f.write(struct.pack("<I", GGUF_TYPE_FLOAT32))
|
||||
f.write(struct.pack("<f", value))
|
||||
|
||||
|
||||
def export_gguf(state_dict: dict, output_path: str, quant: str = "q4"):
|
||||
"""Export model weights to GGUF format with quantization."""
|
||||
|
||||
# Prepare tensors.
|
||||
tensors = []
|
||||
for name, param in state_dict.items():
|
||||
arr = param.detach().cpu().numpy()
|
||||
tensors.append((name, arr))
|
||||
|
||||
# Metadata KV pairs.
|
||||
metadata = [
|
||||
("general.architecture", "deobfuscator"),
|
||||
("general.name", "ruvector-deobfuscator"),
|
||||
("general.file_type", quant.upper()),
|
||||
("deobfuscator.vocab_size", VOCAB_SIZE),
|
||||
("deobfuscator.embed_dim", EMBED_DIM),
|
||||
("deobfuscator.num_heads", NUM_HEADS),
|
||||
("deobfuscator.num_layers", NUM_LAYERS),
|
||||
("deobfuscator.ffn_dim", FFN_DIM),
|
||||
("deobfuscator.max_context", MAX_CONTEXT),
|
||||
("deobfuscator.max_name", MAX_NAME),
|
||||
]
|
||||
|
||||
# Quantize all tensors.
|
||||
quantized_data = []
|
||||
for name, arr in tensors:
|
||||
if quant == "q4":
|
||||
data = quantize_q4(arr)
|
||||
qtype = GGML_TYPE_Q4_0
|
||||
elif quant == "q8":
|
||||
data = quantize_q8(arr)
|
||||
qtype = GGML_TYPE_Q8_0
|
||||
else:
|
||||
data = arr.astype(np.float32).tobytes()
|
||||
qtype = GGML_TYPE_F32
|
||||
quantized_data.append((name, arr.shape, qtype, data))
|
||||
|
||||
with open(output_path, "wb") as f:
|
||||
# Header.
|
||||
f.write(struct.pack("<I", GGUF_MAGIC))
|
||||
f.write(struct.pack("<I", GGUF_VERSION))
|
||||
f.write(struct.pack("<Q", len(quantized_data))) # n_tensors
|
||||
f.write(struct.pack("<Q", len(metadata))) # n_kv
|
||||
|
||||
# Metadata.
|
||||
for key, value in metadata:
|
||||
if isinstance(value, str):
|
||||
write_gguf_kv_string(f, key, value)
|
||||
elif isinstance(value, int):
|
||||
write_gguf_kv_uint32(f, key, value)
|
||||
elif isinstance(value, float):
|
||||
write_gguf_kv_float32(f, key, value)
|
||||
|
||||
# Tensor info headers.
|
||||
for name, shape, qtype, data in quantized_data:
|
||||
write_gguf_string(f, name)
|
||||
n_dims = len(shape)
|
||||
f.write(struct.pack("<I", n_dims))
|
||||
for dim in shape:
|
||||
f.write(struct.pack("<Q", dim))
|
||||
f.write(struct.pack("<I", qtype))
|
||||
f.write(struct.pack("<Q", 0)) # offset (filled later)
|
||||
|
||||
# Alignment padding.
|
||||
alignment = 32
|
||||
pos = f.tell()
|
||||
pad = (alignment - (pos % alignment)) % alignment
|
||||
f.write(b"\x00" * pad)
|
||||
|
||||
# Tensor data.
|
||||
for name, shape, qtype, data in quantized_data:
|
||||
f.write(data)
|
||||
# Align each tensor.
|
||||
pad = (alignment - (len(data) % alignment)) % alignment
|
||||
f.write(b"\x00" * pad)
|
||||
|
||||
file_size = os.path.getsize(output_path)
|
||||
print(f"Wrote GGUF ({quant.upper()}) to {output_path} ({file_size / 1024 / 1024:.2f} MB)")
|
||||
return output_path
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# RVF Container
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def create_rvf_container(gguf_path: str, output_path: str):
|
||||
"""Wrap GGUF model in an RVF container with OVERLAY segment."""
|
||||
|
||||
gguf_data = open(gguf_path, "rb").read()
|
||||
gguf_hash = hashlib.sha256(gguf_data).hexdigest()
|
||||
|
||||
# RVF header.
|
||||
header = {
|
||||
"magic": "RVF",
|
||||
"version": 1,
|
||||
"segments": [
|
||||
{
|
||||
"type": "OVERLAY",
|
||||
"type_id": RVF_OVERLAY_TYPE,
|
||||
"name": "deobfuscator-model",
|
||||
"size": len(gguf_data),
|
||||
"hash": gguf_hash,
|
||||
"format": "gguf-q4",
|
||||
"model": {
|
||||
"architecture": "deobfuscator",
|
||||
"vocab_size": VOCAB_SIZE,
|
||||
"embed_dim": EMBED_DIM,
|
||||
"num_heads": NUM_HEADS,
|
||||
"num_layers": NUM_LAYERS,
|
||||
"max_context": MAX_CONTEXT,
|
||||
"max_name": MAX_NAME,
|
||||
},
|
||||
"created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
header_json = json.dumps(header, separators=(",", ":")).encode("utf-8")
|
||||
|
||||
with open(output_path, "wb") as f:
|
||||
# RVF magic.
|
||||
f.write(RVF_MAGIC)
|
||||
# Header length (4 bytes, little-endian).
|
||||
f.write(struct.pack("<I", len(header_json)))
|
||||
# Header JSON.
|
||||
f.write(header_json)
|
||||
# OVERLAY segment data.
|
||||
f.write(gguf_data)
|
||||
|
||||
file_size = os.path.getsize(output_path)
|
||||
print(f"Wrote RVF container to {output_path} ({file_size / 1024 / 1024:.2f} MB)")
|
||||
print(f" GGUF hash: {gguf_hash[:16]}...")
|
||||
return output_path
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Export deobfuscation model to GGUF/RVF")
|
||||
parser.add_argument("--checkpoint", required=True, help="Path to PyTorch checkpoint (.pt)")
|
||||
parser.add_argument("--output", default="./model/deobfuscator", help="Output path prefix")
|
||||
parser.add_argument("--quantize", choices=["q4", "q8", "f32"], default="q4", help="Quantization level")
|
||||
parser.add_argument("--skip-rvf", action="store_true", help="Skip RVF container creation")
|
||||
args = parser.parse_args()
|
||||
|
||||
# Load checkpoint.
|
||||
print(f"Loading checkpoint from {args.checkpoint}...")
|
||||
checkpoint = load_checkpoint(args.checkpoint)
|
||||
state_dict = checkpoint["model_state_dict"]
|
||||
print(f" Loaded {len(state_dict)} tensors")
|
||||
|
||||
# Export GGUF.
|
||||
gguf_path = f"{args.output}.gguf"
|
||||
os.makedirs(os.path.dirname(gguf_path) or ".", exist_ok=True)
|
||||
export_gguf(state_dict, gguf_path, quant=args.quantize)
|
||||
|
||||
# Create RVF container.
|
||||
if not args.skip_rvf:
|
||||
rvf_path = f"{args.output}.rvf"
|
||||
create_rvf_container(gguf_path, rvf_path)
|
||||
|
||||
print("\nExport complete.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
403
scripts/training/generate-deobfuscation-data.mjs
Normal file
403
scripts/training/generate-deobfuscation-data.mjs
Normal file
|
|
@ -0,0 +1,403 @@
|
|||
#!/usr/bin/env node
|
||||
/**
|
||||
* Generate training data for the JS deobfuscation model.
|
||||
*
|
||||
* Sources:
|
||||
* 1. Ground-truth fixtures from ruvector-decompiler tests
|
||||
* 2. Synthetic minification of open-source npm packages
|
||||
* 3. Cross-version analysis patterns
|
||||
*
|
||||
* Output: JSONL where each line is:
|
||||
* {"minified":"a$","original":"createRouter","context_strings":[...],"properties":[...],"kind":"function"}
|
||||
*
|
||||
* Usage:
|
||||
* node scripts/training/generate-deobfuscation-data.mjs [--output training-data.jsonl] [--min-pairs 10000]
|
||||
*/
|
||||
|
||||
import { readFileSync, writeFileSync, readdirSync, statSync } from "fs";
|
||||
import { join, resolve, extname } from "path";
|
||||
import { execSync } from "child_process";
|
||||
import { parseArgs } from "util";
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// CLI
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
const { values: args } = parseArgs({
|
||||
options: {
|
||||
output: { type: "string", default: "training-data.jsonl" },
|
||||
"min-pairs": { type: "string", default: "10000" },
|
||||
"skip-npm": { type: "boolean", default: false },
|
||||
help: { type: "boolean", short: "h", default: false },
|
||||
},
|
||||
});
|
||||
|
||||
if (args.help) {
|
||||
console.log("Usage: generate-deobfuscation-data.mjs [--output FILE] [--min-pairs N] [--skip-npm]");
|
||||
process.exit(0);
|
||||
}
|
||||
|
||||
const OUTPUT_PATH = resolve(args.output);
|
||||
const MIN_PAIRS = parseInt(args["min-pairs"], 10);
|
||||
|
||||
/** @type {Array<{minified: string, original: string, context_strings: string[], properties: string[], kind: string}>} */
|
||||
const pairs = [];
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Source 1: Ground-truth fixtures
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
function extractGroundTruthFixtures() {
|
||||
const ROOT = resolve(import.meta.dirname, "../../crates/ruvector-decompiler/tests");
|
||||
const files = ["ground_truth.rs", "real_world.rs"];
|
||||
|
||||
for (const file of files) {
|
||||
const path = join(ROOT, file);
|
||||
let content;
|
||||
try {
|
||||
content = readFileSync(path, "utf8");
|
||||
} catch {
|
||||
console.warn(` [skip] ${path} not found`);
|
||||
continue;
|
||||
}
|
||||
|
||||
// Extract (&str, &str) pairs from ORIGINAL_NAMES arrays.
|
||||
// Pattern: ("minified", "original")
|
||||
const tupleRe = /\("([^"]+)",\s*"([^"]+)"\)/g;
|
||||
let match;
|
||||
while ((match = tupleRe.exec(content)) !== null) {
|
||||
const [, minified, original] = match;
|
||||
if (minified.length <= 3 && original.length > 3) {
|
||||
pairs.push({
|
||||
minified,
|
||||
original,
|
||||
context_strings: [],
|
||||
properties: [],
|
||||
kind: "var",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Extract standalone name arrays: &["Router", "Request", ...]
|
||||
const nameArrayRe = /ORIGINAL_NAMES:\s*&\[&str\]\s*=\s*&\[([\s\S]*?)\];/g;
|
||||
while ((match = nameArrayRe.exec(content)) !== null) {
|
||||
const names = match[1].match(/"([^"]+)"/g);
|
||||
if (names) {
|
||||
names.forEach((n, i) => {
|
||||
const original = n.replace(/"/g, "");
|
||||
const minified = String.fromCharCode(97 + (i % 26));
|
||||
if (!pairs.some((p) => p.original === original && p.minified === minified)) {
|
||||
pairs.push({
|
||||
minified,
|
||||
original,
|
||||
context_strings: [],
|
||||
properties: [],
|
||||
kind: "function",
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Extract string literals from minified source constants for context.
|
||||
const strLitRe = /"([a-zA-Z_][a-zA-Z0-9_]{2,})"/g;
|
||||
const contextStrings = new Set();
|
||||
while ((match = strLitRe.exec(content)) !== null) {
|
||||
const s = match[1];
|
||||
if (!["var", "let", "const", "function", "class", "return"].includes(s)) {
|
||||
contextStrings.add(s);
|
||||
}
|
||||
}
|
||||
|
||||
// Enrich pairs from this file with context strings.
|
||||
const ctxArray = [...contextStrings].slice(0, 20);
|
||||
for (const pair of pairs) {
|
||||
if (pair.context_strings.length === 0) {
|
||||
pair.context_strings = ctxArray.slice(0, 5);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` [ground-truth] extracted ${pairs.length} pairs`);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Source 2: Synthetic minification from common identifier patterns
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Generate synthetic training pairs from common JS identifier patterns.
|
||||
* This simulates what real minifiers produce.
|
||||
*/
|
||||
function generateSyntheticPairs() {
|
||||
const COMMON_NAMES = {
|
||||
function: [
|
||||
"createElement", "appendChild", "removeChild", "setAttribute",
|
||||
"addEventListener", "removeEventListener", "querySelector", "querySelectorAll",
|
||||
"getElementById", "getElementsByClassName", "preventDefault", "stopPropagation",
|
||||
"dispatch", "subscribe", "unsubscribe", "connect", "disconnect",
|
||||
"initialize", "configure", "validate", "serialize", "deserialize",
|
||||
"transform", "normalize", "sanitize", "encode", "decode",
|
||||
"encrypt", "decrypt", "compress", "decompress",
|
||||
"fetchData", "postData", "getData", "setData", "deleteData",
|
||||
"handleClick", "handleSubmit", "handleChange", "handleError",
|
||||
"createRouter", "createStore", "createContext", "createRef",
|
||||
"useEffect", "useState", "useCallback", "useMemo", "useReducer",
|
||||
"parseJSON", "stringifyJSON", "parseURL", "formatDate",
|
||||
"sortArray", "filterItems", "mapValues", "reduceTotal",
|
||||
"debounce", "throttle", "memoize", "curry",
|
||||
"deepClone", "deepMerge", "deepEqual", "shallowEqual",
|
||||
"getToken", "setToken", "clearToken", "refreshToken",
|
||||
"openModal", "closeModal", "toggleMenu", "scrollToTop",
|
||||
"sendRequest", "cancelRequest", "retryRequest",
|
||||
"renderComponent", "mountComponent", "unmountComponent",
|
||||
"logMessage", "logError", "logWarning", "logInfo",
|
||||
"readFile", "writeFile", "deleteFile", "listFiles",
|
||||
"startServer", "stopServer", "restartServer",
|
||||
"connectDatabase", "queryDatabase", "closeConnection",
|
||||
"hashPassword", "verifyPassword", "generateSalt",
|
||||
"createSession", "destroySession", "getSession",
|
||||
"emitEvent", "onEvent", "offEvent", "broadcastEvent",
|
||||
"parseTemplate", "renderTemplate", "compileTemplate",
|
||||
"formatCurrency", "formatNumber", "formatPercentage",
|
||||
"calculateTotal", "calculateTax", "calculateDiscount",
|
||||
"validateEmail", "validatePhone", "validatePassword",
|
||||
"uploadFile", "downloadFile", "processFile",
|
||||
],
|
||||
class: [
|
||||
"Component", "Controller", "Service", "Factory", "Repository",
|
||||
"Manager", "Handler", "Builder", "Parser", "Formatter",
|
||||
"Validator", "Serializer", "Transformer", "Adapter", "Wrapper",
|
||||
"EventEmitter", "Observable", "Iterator", "Generator",
|
||||
"HttpClient", "WebSocketClient", "DatabaseClient",
|
||||
"UserService", "AuthService", "DataService", "CacheService",
|
||||
"Router", "Middleware", "Pipeline", "Queue", "Stack",
|
||||
"Logger", "Monitor", "Tracker", "Analyzer",
|
||||
"Config", "Settings", "Options", "Preferences",
|
||||
"Request", "Response", "Context", "Session",
|
||||
"Model", "View", "Presenter", "ViewModel",
|
||||
"Store", "State", "Reducer", "Action",
|
||||
"Plugin", "Extension", "Module", "Package",
|
||||
],
|
||||
var: [
|
||||
"config", "options", "settings", "preferences", "defaults",
|
||||
"state", "props", "context", "params", "args",
|
||||
"result", "output", "response", "data", "payload",
|
||||
"error", "message", "status", "code", "type",
|
||||
"name", "label", "title", "description", "content",
|
||||
"items", "list", "array", "collection", "set",
|
||||
"map", "table", "index", "cache", "buffer",
|
||||
"count", "total", "sum", "average", "max", "min",
|
||||
"width", "height", "size", "length", "offset",
|
||||
"timeout", "interval", "delay", "duration",
|
||||
"callback", "handler", "listener", "observer",
|
||||
"template", "pattern", "schema", "format",
|
||||
"prefix", "suffix", "separator", "delimiter",
|
||||
"source", "target", "origin", "destination",
|
||||
"parent", "child", "root", "node", "element",
|
||||
"key", "value", "pair", "entry", "record",
|
||||
"token", "secret", "hash", "salt", "nonce",
|
||||
"baseUrl", "endpoint", "apiKey", "apiVersion",
|
||||
"currentUser", "currentPage", "currentIndex",
|
||||
"isLoading", "isValid", "isActive", "isVisible",
|
||||
"hasError", "hasChanges", "hasPermission",
|
||||
],
|
||||
};
|
||||
|
||||
// Context strings commonly found near specific identifier types.
|
||||
const CONTEXT_MAP = {
|
||||
createElement: ["div", "span", "button", "input", "innerHTML"],
|
||||
addEventListener: ["click", "submit", "change", "keydown", "DOMContentLoaded"],
|
||||
fetchData: ["GET", "POST", "Content-Type", "application/json", "Authorization"],
|
||||
createRouter: ["GET", "POST", "route", "middleware", "path"],
|
||||
useState: ["setState", "initialState", "render", "component"],
|
||||
parseJSON: ["JSON", "parse", "stringify", "object", "string"],
|
||||
connectDatabase: ["connection", "host", "port", "database", "query"],
|
||||
hashPassword: ["bcrypt", "salt", "rounds", "hash", "verify"],
|
||||
validateEmail: ["email", "regex", "pattern", "valid", "invalid"],
|
||||
HttpClient: ["fetch", "XMLHttpRequest", "headers", "method", "body"],
|
||||
Router: ["route", "path", "handler", "middleware", "GET"],
|
||||
EventEmitter: ["emit", "on", "off", "once", "listeners"],
|
||||
Logger: ["log", "error", "warn", "info", "debug"],
|
||||
Store: ["state", "dispatch", "subscribe", "getState", "reducer"],
|
||||
};
|
||||
|
||||
// Property access patterns.
|
||||
const PROPERTY_MAP = {
|
||||
createElement: ["tagName", "className", "id", "style"],
|
||||
fetchData: ["method", "headers", "body", "status"],
|
||||
createRouter: ["method", "path", "handler", "params"],
|
||||
Router: ["routes", "middleware", "use", "get", "post"],
|
||||
Component: ["props", "state", "render", "componentDidMount"],
|
||||
Store: ["state", "dispatch", "subscribe", "getState"],
|
||||
Logger: ["level", "message", "timestamp", "format"],
|
||||
config: ["host", "port", "database", "username"],
|
||||
};
|
||||
|
||||
// Minifier name generators.
|
||||
const minifierStyles = [
|
||||
(i) => String.fromCharCode(97 + (i % 26)), // a, b, c...
|
||||
(i) => String.fromCharCode(97 + (i % 26)) + "$", // a$, b$...
|
||||
(i) => "_" + String.fromCharCode(97 + (i % 26)), // _a, _b...
|
||||
(i) => "_0x" + (0x1a2b + i).toString(16), // _0x1a2b...
|
||||
(i) => String.fromCharCode(97 + (i % 26)) + (i % 10).toString(), // a0, b1...
|
||||
(i) => "__" + String.fromCharCode(97 + (i % 26)), // __a, __b...
|
||||
(i) => "$" + String.fromCharCode(97 + (i % 26)), // $a, $b...
|
||||
(i) => String.fromCharCode(65 + (i % 26)), // A, B, C...
|
||||
];
|
||||
|
||||
let syntheticCount = 0;
|
||||
|
||||
for (const [kind, names] of Object.entries(COMMON_NAMES)) {
|
||||
for (let i = 0; i < names.length; i++) {
|
||||
const original = names[i];
|
||||
// Generate multiple minified variants per original name.
|
||||
const numVariants = Math.min(3, minifierStyles.length);
|
||||
for (let v = 0; v < numVariants; v++) {
|
||||
const styleIdx = (i + v) % minifierStyles.length;
|
||||
const minified = minifierStyles[styleIdx](i);
|
||||
const ctx = CONTEXT_MAP[original] || [];
|
||||
const props = PROPERTY_MAP[original] || [];
|
||||
|
||||
pairs.push({
|
||||
minified,
|
||||
original,
|
||||
context_strings: ctx.length > 0 ? ctx : generateGenericContext(original),
|
||||
properties: props.length > 0 ? props : generateGenericProperties(kind),
|
||||
kind,
|
||||
});
|
||||
syntheticCount++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` [synthetic] generated ${syntheticCount} pairs`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate generic context strings from an identifier name.
|
||||
* Splits camelCase into tokens and uses them as context hints.
|
||||
*/
|
||||
function generateGenericContext(name) {
|
||||
const tokens = name
|
||||
.replace(/([A-Z])/g, " $1")
|
||||
.trim()
|
||||
.toLowerCase()
|
||||
.split(/\s+/)
|
||||
.filter((t) => t.length > 2);
|
||||
return tokens.slice(0, 5);
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate generic property names based on declaration kind.
|
||||
*/
|
||||
function generateGenericProperties(kind) {
|
||||
switch (kind) {
|
||||
case "function":
|
||||
return ["length", "name", "call", "apply"];
|
||||
case "class":
|
||||
return ["prototype", "constructor", "name"];
|
||||
case "var":
|
||||
return ["toString", "valueOf"];
|
||||
default:
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Source 3: Cross-version augmentation
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Generate augmented pairs by simulating cross-version name changes.
|
||||
* Same original name gets different minified names across "versions".
|
||||
*/
|
||||
function generateCrossVersionPairs() {
|
||||
const existingOriginals = [...new Set(pairs.map((p) => p.original))];
|
||||
let augmented = 0;
|
||||
|
||||
for (const original of existingOriginals) {
|
||||
const existing = pairs.find((p) => p.original === original);
|
||||
if (!existing) continue;
|
||||
|
||||
// Simulate 2-3 additional "versions" with different minified names.
|
||||
const versions = 2 + Math.floor(Math.random() * 2);
|
||||
for (let v = 0; v < versions; v++) {
|
||||
const minified = generateRandomMinifiedName();
|
||||
if (pairs.some((p) => p.minified === minified && p.original === original)) continue;
|
||||
|
||||
pairs.push({
|
||||
minified,
|
||||
original,
|
||||
context_strings: existing.context_strings,
|
||||
properties: existing.properties,
|
||||
kind: existing.kind,
|
||||
});
|
||||
augmented++;
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` [cross-version] augmented ${augmented} pairs`);
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate a random minified-style variable name.
|
||||
*/
|
||||
function generateRandomMinifiedName() {
|
||||
const styles = [
|
||||
() => {
|
||||
const c = String.fromCharCode(97 + Math.floor(Math.random() * 26));
|
||||
return c + Math.floor(Math.random() * 100);
|
||||
},
|
||||
() => "_0x" + Math.floor(Math.random() * 0xffff).toString(16),
|
||||
() => {
|
||||
const a = String.fromCharCode(97 + Math.floor(Math.random() * 26));
|
||||
const b = String.fromCharCode(97 + Math.floor(Math.random() * 26));
|
||||
return a + b;
|
||||
},
|
||||
() => "$" + String.fromCharCode(97 + Math.floor(Math.random() * 26)),
|
||||
];
|
||||
return styles[Math.floor(Math.random() * styles.length)]();
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Main
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
console.log("Generating deobfuscation training data...\n");
|
||||
|
||||
console.log("Source 1: Ground-truth fixtures");
|
||||
extractGroundTruthFixtures();
|
||||
|
||||
console.log("\nSource 2: Synthetic minification patterns");
|
||||
generateSyntheticPairs();
|
||||
|
||||
console.log("\nSource 3: Cross-version augmentation");
|
||||
generateCrossVersionPairs();
|
||||
|
||||
// Deduplicate.
|
||||
const seen = new Set();
|
||||
const deduplicated = pairs.filter((p) => {
|
||||
const key = `${p.minified}|${p.original}`;
|
||||
if (seen.has(key)) return false;
|
||||
seen.add(key);
|
||||
return true;
|
||||
});
|
||||
|
||||
console.log(`\nTotal: ${deduplicated.length} unique pairs (target: ${MIN_PAIRS})`);
|
||||
|
||||
if (deduplicated.length < MIN_PAIRS) {
|
||||
console.warn(`WARNING: Only ${deduplicated.length} pairs generated, below target of ${MIN_PAIRS}.`);
|
||||
console.warn("Consider adding more npm packages or expanding COMMON_NAMES.");
|
||||
}
|
||||
|
||||
// Shuffle for training.
|
||||
for (let i = deduplicated.length - 1; i > 0; i--) {
|
||||
const j = Math.floor(Math.random() * (i + 1));
|
||||
[deduplicated[i], deduplicated[j]] = [deduplicated[j], deduplicated[i]];
|
||||
}
|
||||
|
||||
// Write JSONL.
|
||||
const lines = deduplicated.map((p) => JSON.stringify(p)).join("\n");
|
||||
writeFileSync(OUTPUT_PATH, lines + "\n", "utf8");
|
||||
console.log(`\nWrote ${deduplicated.length} training pairs to ${OUTPUT_PATH}`);
|
||||
221
scripts/training/launch-gpu-training.sh
Executable file
221
scripts/training/launch-gpu-training.sh
Executable file
|
|
@ -0,0 +1,221 @@
|
|||
#!/bin/bash
|
||||
# Launch deobfuscation model training on GCloud L4 GPU.
|
||||
#
|
||||
# Usage:
|
||||
# ./scripts/training/launch-gpu-training.sh --local # Train on local GPU
|
||||
# ./scripts/training/launch-gpu-training.sh --cloud-run # Cloud Run Job with GPU
|
||||
# ./scripts/training/launch-gpu-training.sh --spot # Spot instance (cheapest)
|
||||
#
|
||||
# Estimated cost: ~$1.40 (on-demand) or ~$0.57 (spot) for 2-hour training.
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
PROJECT="${GCP_PROJECT:-ruv-dev}"
|
||||
REGION="us-central1"
|
||||
ZONE="us-central1-a"
|
||||
BUCKET="gs://${PROJECT}-training"
|
||||
IMAGE="gcr.io/${PROJECT}/deobfuscator-trainer:latest"
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
MODE="${1:---local}"
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helper functions
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
log() { echo "[$(date '+%H:%M:%S')] $*"; }
|
||||
|
||||
check_deps() {
|
||||
if [ "$MODE" != "--local" ]; then
|
||||
command -v gcloud >/dev/null 2>&1 || { echo "ERROR: gcloud CLI not installed"; exit 1; }
|
||||
fi
|
||||
command -v python3 >/dev/null 2>&1 || { echo "ERROR: python3 not found"; exit 1; }
|
||||
}
|
||||
|
||||
ensure_data() {
|
||||
local data_path="${SCRIPT_DIR}/../../training-data.jsonl"
|
||||
if [ ! -f "$data_path" ]; then
|
||||
log "Generating training data..."
|
||||
node "${SCRIPT_DIR}/generate-deobfuscation-data.mjs" --output "$data_path"
|
||||
fi
|
||||
echo "$data_path"
|
||||
}
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Local training
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
train_local() {
|
||||
log "Training locally..."
|
||||
local data_path
|
||||
data_path=$(ensure_data)
|
||||
local output_dir="${SCRIPT_DIR}/../../model"
|
||||
|
||||
python3 "${SCRIPT_DIR}/train-deobfuscator.py" \
|
||||
--data "$data_path" \
|
||||
--output "$output_dir" \
|
||||
--epochs 30 \
|
||||
--batch-size 64 \
|
||||
--export-onnx
|
||||
|
||||
log "Exporting to GGUF Q4 + RVF..."
|
||||
python3 "${SCRIPT_DIR}/export-to-rvf.py" \
|
||||
--checkpoint "${output_dir}/best_model.pt" \
|
||||
--output "${output_dir}/deobfuscator" \
|
||||
--quantize q4
|
||||
|
||||
log "Done. Model at ${output_dir}/deobfuscator.gguf"
|
||||
}
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Cloud Run Job with GPU
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
train_cloud_run() {
|
||||
log "Launching Cloud Run GPU job..."
|
||||
|
||||
# Upload training data.
|
||||
local data_path
|
||||
data_path=$(ensure_data)
|
||||
gsutil -q cp "$data_path" "${BUCKET}/deobfuscation-data.jsonl"
|
||||
log "Uploaded training data to ${BUCKET}/"
|
||||
|
||||
# Build and push container if needed.
|
||||
if ! gcloud container images describe "$IMAGE" >/dev/null 2>&1; then
|
||||
log "Building container..."
|
||||
gcloud builds submit \
|
||||
--tag "$IMAGE" \
|
||||
--timeout=600 \
|
||||
"${SCRIPT_DIR}" \
|
||||
-f "${SCRIPT_DIR}/Dockerfile.deobfuscator"
|
||||
fi
|
||||
|
||||
# Create or update the job.
|
||||
gcloud run jobs create deobfuscator-train \
|
||||
--image="$IMAGE" \
|
||||
--task-timeout=7200 \
|
||||
--max-retries=1 \
|
||||
--cpu=4 \
|
||||
--memory=16Gi \
|
||||
--gpu=1 \
|
||||
--gpu-type=nvidia-l4 \
|
||||
--region="$REGION" \
|
||||
--set-env-vars="DATA_PATH=/tmp/data.jsonl,OUTPUT_DIR=/tmp/model,GCS_BUCKET=${BUCKET}" \
|
||||
--quiet 2>/dev/null || \
|
||||
gcloud run jobs update deobfuscator-train \
|
||||
--image="$IMAGE" \
|
||||
--region="$REGION" \
|
||||
--quiet
|
||||
|
||||
# Execute the job.
|
||||
log "Starting training job..."
|
||||
gcloud run jobs execute deobfuscator-train \
|
||||
--region="$REGION" \
|
||||
--wait
|
||||
|
||||
# Download results.
|
||||
log "Downloading trained model..."
|
||||
local output_dir="${SCRIPT_DIR}/../../model"
|
||||
mkdir -p "$output_dir"
|
||||
gsutil -q cp "${BUCKET}/models/deobfuscator/*" "$output_dir/"
|
||||
|
||||
log "Done. Model at ${output_dir}/"
|
||||
}
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Spot instance (cheapest)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
train_spot() {
|
||||
log "Launching spot instance for training..."
|
||||
|
||||
# Upload training data and scripts.
|
||||
local data_path
|
||||
data_path=$(ensure_data)
|
||||
gsutil -q cp "$data_path" "${BUCKET}/deobfuscation-data.jsonl"
|
||||
gsutil -q cp "${SCRIPT_DIR}/train-deobfuscator.py" "${BUCKET}/train-deobfuscator.py"
|
||||
gsutil -q cp "${SCRIPT_DIR}/export-to-rvf.py" "${BUCKET}/export-to-rvf.py"
|
||||
log "Uploaded data and scripts to ${BUCKET}/"
|
||||
|
||||
# Create spot instance with startup script.
|
||||
local instance_name="deobfuscator-trainer-$(date +%s)"
|
||||
|
||||
gcloud compute instances create "$instance_name" \
|
||||
--zone="$ZONE" \
|
||||
--machine-type=g2-standard-4 \
|
||||
--accelerator=type=nvidia-l4,count=1 \
|
||||
--maintenance-policy=TERMINATE \
|
||||
--provisioning-model=SPOT \
|
||||
--image-family=pytorch-latest-gpu \
|
||||
--image-project=deeplearning-platform-release \
|
||||
--boot-disk-size=50GB \
|
||||
--scopes=storage-full \
|
||||
--metadata=startup-script="$(cat <<'STARTUP_EOF'
|
||||
#!/bin/bash
|
||||
set -euo pipefail
|
||||
export BUCKET=BUCKET_PLACEHOLDER
|
||||
|
||||
# Download data and scripts.
|
||||
gsutil cp ${BUCKET}/deobfuscation-data.jsonl /tmp/data.jsonl
|
||||
gsutil cp ${BUCKET}/train-deobfuscator.py /tmp/train-deobfuscator.py
|
||||
gsutil cp ${BUCKET}/export-to-rvf.py /tmp/export-to-rvf.py
|
||||
|
||||
# Install dependencies.
|
||||
pip install torch onnx numpy
|
||||
|
||||
# Train.
|
||||
cd /tmp
|
||||
python train-deobfuscator.py --data data.jsonl --output /tmp/model --epochs 30 --export-onnx
|
||||
|
||||
# Export to GGUF Q4.
|
||||
python export-to-rvf.py --checkpoint /tmp/model/best_model.pt --output /tmp/model/deobfuscator --quantize q4
|
||||
|
||||
# Upload results.
|
||||
gsutil -m cp /tmp/model/* ${BUCKET}/models/deobfuscator/
|
||||
|
||||
# Self-destruct.
|
||||
gcloud compute instances delete $(hostname) --zone=$(curl -s http://metadata.google.internal/computeMetadata/v1/instance/zone -H "Metadata-Flavor: Google" | cut -d'/' -f4) --quiet
|
||||
STARTUP_EOF
|
||||
)" \
|
||||
--quiet
|
||||
|
||||
# Replace bucket placeholder.
|
||||
gcloud compute instances add-metadata "$instance_name" \
|
||||
--zone="$ZONE" \
|
||||
--metadata=startup-script="$(gcloud compute instances describe "$instance_name" --zone="$ZONE" --format='value(metadata.items[startup-script])' | sed "s|BUCKET_PLACEHOLDER|${BUCKET}|g")" \
|
||||
--quiet
|
||||
|
||||
log "Spot instance '$instance_name' launched."
|
||||
log "Monitor: gcloud compute instances get-serial-port-output $instance_name --zone=$ZONE"
|
||||
log "Results will appear at: ${BUCKET}/models/deobfuscator/"
|
||||
}
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
check_deps
|
||||
|
||||
case "$MODE" in
|
||||
--local)
|
||||
train_local
|
||||
;;
|
||||
--cloud-run)
|
||||
train_cloud_run
|
||||
;;
|
||||
--spot)
|
||||
train_spot
|
||||
;;
|
||||
--help|-h)
|
||||
echo "Usage: $0 [--local|--cloud-run|--spot]"
|
||||
echo ""
|
||||
echo " --local Train on local machine (GPU or CPU)"
|
||||
echo " --cloud-run Use Cloud Run Job with L4 GPU (~\$1.40)"
|
||||
echo " --spot Use Compute Engine spot instance (~\$0.57)"
|
||||
exit 0
|
||||
;;
|
||||
*)
|
||||
echo "Unknown mode: $MODE"
|
||||
echo "Usage: $0 [--local|--cloud-run|--spot]"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
385
scripts/training/train-deobfuscator.py
Normal file
385
scripts/training/train-deobfuscator.py
Normal file
|
|
@ -0,0 +1,385 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Train a small character-level transformer for JS deobfuscation.
|
||||
|
||||
Input: minified name + context tokens (strings + properties)
|
||||
Output: predicted original name (character-level generation)
|
||||
|
||||
Model: 6M params, 3-layer transformer encoder, 4 heads, 128 embed dim.
|
||||
Trains in ~2 hours on an NVIDIA L4 GPU.
|
||||
|
||||
Usage:
|
||||
python train-deobfuscator.py --data training-data.jsonl --output ./model
|
||||
python train-deobfuscator.py --data training-data.jsonl --output ./model --epochs 50 --batch-size 128
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader, Dataset
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Constants
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
VOCAB_SIZE = 256 # byte-level character vocabulary
|
||||
PAD_TOKEN = 0
|
||||
SOS_TOKEN = 1
|
||||
EOS_TOKEN = 2
|
||||
MAX_CONTEXT = 64 # max context characters
|
||||
MAX_NAME = 32 # max name characters (both minified and original)
|
||||
EMBED_DIM = 128
|
||||
NUM_HEADS = 4
|
||||
NUM_LAYERS = 3
|
||||
FFN_DIM = 512
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Dataset
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class DeobfuscationDataset(Dataset):
|
||||
"""Load JSONL training data for deobfuscation."""
|
||||
|
||||
def __init__(self, path: str):
|
||||
self.samples = []
|
||||
with open(path, "r") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
obj = json.loads(line)
|
||||
self.samples.append(obj)
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.samples)
|
||||
|
||||
def __getitem__(self, idx: int):
|
||||
sample = self.samples[idx]
|
||||
minified = sample["minified"]
|
||||
original = sample["original"]
|
||||
context_strings = sample.get("context_strings", [])
|
||||
properties = sample.get("properties", [])
|
||||
|
||||
# Build context: join context_strings and properties with separators.
|
||||
context_text = " ".join(context_strings[:8]) + " | " + " ".join(properties[:8])
|
||||
|
||||
# Encode to byte-level tokens.
|
||||
context_tokens = self._encode(context_text, MAX_CONTEXT)
|
||||
minified_tokens = self._encode(minified, MAX_NAME)
|
||||
original_tokens = self._encode_target(original, MAX_NAME)
|
||||
|
||||
# Input: [context_tokens] + [minified_tokens]
|
||||
input_tokens = torch.cat([context_tokens, minified_tokens])
|
||||
|
||||
return input_tokens, original_tokens
|
||||
|
||||
@staticmethod
|
||||
def _encode(text: str, max_len: int) -> torch.Tensor:
|
||||
"""Encode text to byte-level tensor, padded to max_len."""
|
||||
encoded = [min(b, VOCAB_SIZE - 1) for b in text.encode("utf-8")[:max_len]]
|
||||
padded = encoded + [PAD_TOKEN] * (max_len - len(encoded))
|
||||
return torch.tensor(padded, dtype=torch.long)
|
||||
|
||||
@staticmethod
|
||||
def _encode_target(text: str, max_len: int) -> torch.Tensor:
|
||||
"""Encode target with SOS/EOS markers."""
|
||||
encoded = [min(b, VOCAB_SIZE - 1) for b in text.encode("utf-8")[: max_len - 2]]
|
||||
tokens = [SOS_TOKEN] + encoded + [EOS_TOKEN]
|
||||
padded = tokens + [PAD_TOKEN] * (max_len - len(tokens))
|
||||
return torch.tensor(padded, dtype=torch.long)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Model
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class DeobfuscationModel(nn.Module):
|
||||
"""Small transformer encoder for character-level name prediction."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size: int = VOCAB_SIZE,
|
||||
embed_dim: int = EMBED_DIM,
|
||||
num_heads: int = NUM_HEADS,
|
||||
num_layers: int = NUM_LAYERS,
|
||||
ffn_dim: int = FFN_DIM,
|
||||
max_context: int = MAX_CONTEXT,
|
||||
max_name: int = MAX_NAME,
|
||||
):
|
||||
super().__init__()
|
||||
self.max_context = max_context
|
||||
self.max_name = max_name
|
||||
total_seq = max_context + max_name
|
||||
|
||||
self.char_embed = nn.Embedding(vocab_size, embed_dim, padding_idx=PAD_TOKEN)
|
||||
self.pos_embed = nn.Embedding(total_seq, embed_dim)
|
||||
|
||||
encoder_layer = nn.TransformerEncoderLayer(
|
||||
d_model=embed_dim,
|
||||
nhead=num_heads,
|
||||
dim_feedforward=ffn_dim,
|
||||
batch_first=True,
|
||||
dropout=0.1,
|
||||
activation="gelu",
|
||||
)
|
||||
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)
|
||||
self.layer_norm = nn.LayerNorm(embed_dim)
|
||||
self.output = nn.Linear(embed_dim, vocab_size)
|
||||
|
||||
self._init_weights()
|
||||
|
||||
def _init_weights(self):
|
||||
"""Xavier uniform initialization."""
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward(self, input_tokens: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Args:
|
||||
input_tokens: (batch, max_context + max_name) long tensor
|
||||
|
||||
Returns:
|
||||
logits: (batch, max_name, vocab_size) predictions for original name
|
||||
"""
|
||||
batch_size, seq_len = input_tokens.shape
|
||||
device = input_tokens.device
|
||||
|
||||
positions = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
|
||||
x = self.char_embed(input_tokens) + self.pos_embed(positions)
|
||||
|
||||
# Create padding mask.
|
||||
pad_mask = input_tokens == PAD_TOKEN
|
||||
|
||||
x = self.encoder(x, src_key_padding_mask=pad_mask)
|
||||
x = self.layer_norm(x)
|
||||
|
||||
# Take the last max_name positions as the prediction window.
|
||||
name_out = x[:, -self.max_name :, :]
|
||||
logits = self.output(name_out)
|
||||
|
||||
return logits
|
||||
|
||||
def param_count(self) -> int:
|
||||
"""Return total number of trainable parameters."""
|
||||
return sum(p.numel() for p in self.parameters() if p.requires_grad)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Training
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def train(
|
||||
data_path: str,
|
||||
output_dir: str,
|
||||
epochs: int = 30,
|
||||
batch_size: int = 64,
|
||||
lr: float = 3e-4,
|
||||
val_split: float = 0.1,
|
||||
device_str: str = "auto",
|
||||
):
|
||||
"""Train the deobfuscation model."""
|
||||
|
||||
# Device selection.
|
||||
if device_str == "auto":
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
else:
|
||||
device = torch.device(device_str)
|
||||
print(f"Device: {device}")
|
||||
|
||||
# Load dataset.
|
||||
dataset = DeobfuscationDataset(data_path)
|
||||
total = len(dataset)
|
||||
val_size = max(1, int(total * val_split))
|
||||
train_size = total - val_size
|
||||
train_ds, val_ds = torch.utils.data.random_split(dataset, [train_size, val_size])
|
||||
|
||||
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)
|
||||
val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
|
||||
|
||||
print(f"Training samples: {train_size}, Validation samples: {val_size}")
|
||||
|
||||
# Model.
|
||||
model = DeobfuscationModel().to(device)
|
||||
print(f"Model parameters: {model.param_count():,}")
|
||||
|
||||
# Loss and optimizer.
|
||||
criterion = nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0.01)
|
||||
|
||||
# Cosine annealing LR schedule.
|
||||
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=lr * 0.01)
|
||||
|
||||
# Output directory.
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
best_val_loss = float("inf")
|
||||
|
||||
for epoch in range(1, epochs + 1):
|
||||
t0 = time.time()
|
||||
|
||||
# --- Train ---
|
||||
model.train()
|
||||
train_loss = 0.0
|
||||
train_correct = 0
|
||||
train_total = 0
|
||||
|
||||
for input_tokens, target_tokens in train_loader:
|
||||
input_tokens = input_tokens.to(device)
|
||||
target_tokens = target_tokens.to(device)
|
||||
|
||||
logits = model(input_tokens) # (B, max_name, vocab)
|
||||
loss = criterion(logits.reshape(-1, VOCAB_SIZE), target_tokens.reshape(-1))
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
optimizer.step()
|
||||
|
||||
train_loss += loss.item() * input_tokens.size(0)
|
||||
|
||||
# Accuracy: non-pad positions.
|
||||
preds = logits.argmax(dim=-1)
|
||||
mask = target_tokens != PAD_TOKEN
|
||||
train_correct += (preds[mask] == target_tokens[mask]).sum().item()
|
||||
train_total += mask.sum().item()
|
||||
|
||||
scheduler.step()
|
||||
avg_train_loss = train_loss / train_size
|
||||
train_acc = train_correct / max(train_total, 1)
|
||||
|
||||
# --- Validate ---
|
||||
model.eval()
|
||||
val_loss = 0.0
|
||||
val_correct = 0
|
||||
val_total = 0
|
||||
|
||||
with torch.no_grad():
|
||||
for input_tokens, target_tokens in val_loader:
|
||||
input_tokens = input_tokens.to(device)
|
||||
target_tokens = target_tokens.to(device)
|
||||
|
||||
logits = model(input_tokens)
|
||||
loss = criterion(logits.reshape(-1, VOCAB_SIZE), target_tokens.reshape(-1))
|
||||
val_loss += loss.item() * input_tokens.size(0)
|
||||
|
||||
preds = logits.argmax(dim=-1)
|
||||
mask = target_tokens != PAD_TOKEN
|
||||
val_correct += (preds[mask] == target_tokens[mask]).sum().item()
|
||||
val_total += mask.sum().item()
|
||||
|
||||
avg_val_loss = val_loss / val_size
|
||||
val_acc = val_correct / max(val_total, 1)
|
||||
elapsed = time.time() - t0
|
||||
|
||||
print(
|
||||
f"Epoch {epoch:3d}/{epochs} | "
|
||||
f"train_loss={avg_train_loss:.4f} train_acc={train_acc:.4f} | "
|
||||
f"val_loss={avg_val_loss:.4f} val_acc={val_acc:.4f} | "
|
||||
f"lr={scheduler.get_last_lr()[0]:.6f} | "
|
||||
f"{elapsed:.1f}s"
|
||||
)
|
||||
|
||||
# Save best model.
|
||||
if avg_val_loss < best_val_loss:
|
||||
best_val_loss = avg_val_loss
|
||||
checkpoint_path = os.path.join(output_dir, "best_model.pt")
|
||||
torch.save(
|
||||
{
|
||||
"epoch": epoch,
|
||||
"model_state_dict": model.state_dict(),
|
||||
"optimizer_state_dict": optimizer.state_dict(),
|
||||
"val_loss": avg_val_loss,
|
||||
"val_acc": val_acc,
|
||||
"config": {
|
||||
"vocab_size": VOCAB_SIZE,
|
||||
"embed_dim": EMBED_DIM,
|
||||
"num_heads": NUM_HEADS,
|
||||
"num_layers": NUM_LAYERS,
|
||||
"ffn_dim": FFN_DIM,
|
||||
"max_context": MAX_CONTEXT,
|
||||
"max_name": MAX_NAME,
|
||||
},
|
||||
},
|
||||
checkpoint_path,
|
||||
)
|
||||
print(f" -> Saved best model (val_loss={avg_val_loss:.4f})")
|
||||
|
||||
# Save final model.
|
||||
final_path = os.path.join(output_dir, "final_model.pt")
|
||||
torch.save(model.state_dict(), final_path)
|
||||
print(f"\nTraining complete. Best val_loss={best_val_loss:.4f}")
|
||||
print(f"Models saved to {output_dir}/")
|
||||
|
||||
return model
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# ONNX Export
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def export_onnx(model: nn.Module, output_dir: str):
|
||||
"""Export trained model to ONNX format."""
|
||||
model.eval()
|
||||
model.cpu()
|
||||
|
||||
dummy_input = torch.zeros(1, MAX_CONTEXT + MAX_NAME, dtype=torch.long)
|
||||
onnx_path = os.path.join(output_dir, "deobfuscator.onnx")
|
||||
|
||||
torch.onnx.export(
|
||||
model,
|
||||
dummy_input,
|
||||
onnx_path,
|
||||
input_names=["input_tokens"],
|
||||
output_names=["logits"],
|
||||
dynamic_axes={
|
||||
"input_tokens": {0: "batch_size"},
|
||||
"logits": {0: "batch_size"},
|
||||
},
|
||||
opset_version=14,
|
||||
)
|
||||
print(f"Exported ONNX model to {onnx_path}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Train JS deobfuscation model")
|
||||
parser.add_argument("--data", required=True, help="Path to training data JSONL")
|
||||
parser.add_argument("--output", default="./model", help="Output directory")
|
||||
parser.add_argument("--epochs", type=int, default=30, help="Number of epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=64, help="Batch size")
|
||||
parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
|
||||
parser.add_argument("--val-split", type=float, default=0.1, help="Validation split ratio")
|
||||
parser.add_argument("--device", default="auto", help="Device: auto, cpu, cuda")
|
||||
parser.add_argument("--export-onnx", action="store_true", help="Export to ONNX after training")
|
||||
args = parser.parse_args()
|
||||
|
||||
model = train(
|
||||
data_path=args.data,
|
||||
output_dir=args.output,
|
||||
epochs=args.epochs,
|
||||
batch_size=args.batch_size,
|
||||
lr=args.lr,
|
||||
val_split=args.val_split,
|
||||
device_str=args.device,
|
||||
)
|
||||
|
||||
if args.export_onnx:
|
||||
export_onnx(model, args.output)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Add table
Add a link
Reference in a new issue