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289 lines
11 KiB
Python
289 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Neural Delta GCL Compressor (Optional Second Stage)
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VAE-style neural compression layered on top of Delta GCL.
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Per canonical lock-in: Delta GCL is lawful base codec, neural layer is learned transport compressor.
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Canonical Field Equation:
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q_θ(z | x) = N( μ_θ(x), diag(σ²_θ(x)) )
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z = μ_θ(x) + σ_θ(x) ⊙ ε, ε ~ N(0, I)
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x̂ = g_φ(z)
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L = D(x, x̂) + β · KL(q_θ(z | x) || N(0, I))
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R_total = R_ΔGCL · R_neural
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Architecture:
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raw metadata m → DeltaGCL(m) = x → q_θ(z | x) → z → x̂ = g_φ(z)
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→ verify x̂ ≈ x → verify DeltaGCLDecode(x̂) preserves invariant → commit or refuse
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Per AGENTS.md: This is a shim layer - logic in Lean, only JSON marshaling here.
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"""
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import sys
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from pathlib import Path
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# Add project root to Python path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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import json
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import hashlib
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from typing import Dict, Any, Optional, Tuple
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from dataclasses import dataclass
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import numpy as np
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from infra.delta_gcl_compression_service import DeltaGCLCompressionService, CompressionResult
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@dataclass
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class NeuralCompressionResult:
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"""Neural compression result"""
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latent: np.ndarray # 64-dimensional latent representation
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reconstructed_delta_gcl: str
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neural_ratio: float
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total_ratio: float
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verified: bool
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verification_error: Optional[str]
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class NeuralDeltaGCLCompressor:
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"""
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VAE-style neural compressor for Delta GCL sequences.
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This is an optional second stage that compresses Delta GCL output further.
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Per canonical lock-in: Delta GCL remains the lawful base codec.
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"""
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def __init__(self, latent_dim: int = 64):
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self.latent_dim = latent_dim
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self.delta_gcl_service = DeltaGCLCompressionService()
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# Model parameters (placeholder - would be loaded from trained model)
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self.encoder_mean = None # μ_θ
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self.encoder_std = None # σ_θ
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self.decoder_weights = None # g_φ
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self.beta = 1e-3 # KL regularization weight
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# Model state
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self.is_trained = False
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self.model_version = "0.1.0-placeholder"
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def encode(self, delta_gcl: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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Encode Delta GCL sequence to latent representation.
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q_θ(z | x) = N( μ_θ(x), diag(σ²_θ(x)) )
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z = μ_θ(x) + σ_θ(x) ⊙ ε, ε ~ N(0, I)
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Returns: (μ, σ, z)
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"""
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if not self.is_trained:
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# Placeholder: use hash-based encoding until model is trained
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hash_bytes = hashlib.sha256(delta_gcl.encode()).digest()
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# Convert first 64 bytes to float array
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mu = np.frombuffer(hash_bytes[:self.latent_dim * 4], dtype=np.float32)
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if len(mu) < self.latent_dim:
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mu = np.pad(mu, (0, self.latent_dim - len(mu)))
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sigma = np.ones(self.latent_dim) * 0.1 # Fixed std for placeholder
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else:
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# Actual VAE encoder would be called here
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mu, sigma = self._encode_with_model(delta_gcl)
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# Reparameterization trick
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epsilon = np.random.randn(self.latent_dim)
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z = mu + sigma * epsilon
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return mu, sigma, z
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def _encode_with_model(self, delta_gcl: str) -> Tuple[np.ndarray, np.ndarray]:
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"""Encode using actual trained VAE encoder (not implemented yet)"""
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# This would call the actual neural network
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# For now, return placeholder
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return np.zeros(self.latent_dim), np.ones(self.latent_dim)
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def decode(self, z: np.ndarray) -> str:
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"""
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Decode latent representation back to Delta GCL sequence.
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x̂ = g_φ(z)
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"""
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if not self.is_trained:
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# Placeholder: use deterministic hash-based decoding
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z_bytes = z.tobytes()[:32]
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hash_val = hashlib.sha256(z_bytes).hexdigest()
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# Reconstruct Delta GCL format (simplified)
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return f"F{hash_val[:8]}"
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else:
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# Actual VAE decoder would be called here
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return self._decode_with_model(z)
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def _decode_with_model(self, z: np.ndarray) -> str:
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"""Decode using actual trained VAE decoder (not implemented yet)"""
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# This would call the actual neural network
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return ""
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def compress_with_neural(self, manifest: Dict[str, Any],
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manifest_id: str,
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verify: bool = True) -> NeuralCompressionResult:
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"""
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Compress manifest using two-stage process:
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1. Delta GCL (lawful base codec)
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2. Neural compression (learned transport compressor)
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Canonical stack:
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raw metadata m → DeltaGCL(m) = x → q_θ(z | x) → z → x̂ = g_φ(z)
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→ verify x̂ ≈ x → verify DeltaGCLDecode(x̂) preserves invariant → commit or refuse
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"""
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# Stage 1: Delta GCL compression (lawful base codec)
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delta_gcl_result = self.delta_gcl_service.compress_manifest(
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manifest, manifest_id, use_delta=True, verify=True
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)
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if not delta_gcl_result.verified:
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return NeuralCompressionResult(
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latent=np.zeros(self.latent_dim),
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reconstructed_delta_gcl="",
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neural_ratio=1.0,
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total_ratio=delta_gcl_result.stats["compressed_size"] / delta_gcl_result.stats["original_size"],
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verified=False,
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verification_error=f"Delta GCL verification failed: {delta_gcl_result.verification_error}"
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)
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delta_gcl = delta_gcl_result.delta_gcl
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original_size = delta_gcl_result.stats["original_size"]
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delta_gcl_size = delta_gcl_result.stats["compressed_size"]
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# Stage 2: Neural compression (learned transport compressor)
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mu, sigma, z = self.encode(delta_gcl)
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# Calculate neural compression ratio
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latent_size = self.latent_dim * 4 # 4 bytes per float32
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neural_ratio = latent_size / delta_gcl_size
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# Decode latent back to Delta GCL
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reconstructed_delta_gcl = self.decode(z)
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# Verification: check x̂ ≈ x
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reconstruction_match = self._verify_reconstruction(delta_gcl, reconstructed_delta_gcl)
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# Verification: check DeltaGCLDecode(x̂) preserves invariant
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if verify and reconstruction_match:
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# Decode reconstructed Delta GCL and check invariants
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verified, error = self.delta_gcl_service.verify_compression(
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reconstructed_delta_gcl, manifest
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)
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else:
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verified = False
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error = "Reconstruction verification failed" if not reconstruction_match else None
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# Total compression ratio
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total_ratio = latent_size / original_size
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return NeuralCompressionResult(
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latent=z,
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reconstructed_delta_gcl=reconstructed_delta_gcl,
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neural_ratio=neural_ratio,
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total_ratio=total_ratio,
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verified=verified,
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verification_error=error
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)
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def _verify_reconstruction(self, original: str, reconstructed: str) -> bool:
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"""Verify that reconstructed Delta GCL matches original (x̂ ≈ x)"""
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if not self.is_trained:
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# Placeholder: accept reconstruction for untrained model
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return True
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# For trained model, check if reconstructed is close to original
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# This would use a similarity metric (e.g., BLEU, edit distance)
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# For now, simple equality check
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return original == reconstructed
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def compute_loss(self, original: str, reconstructed: str,
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mu: np.ndarray, sigma: np.ndarray) -> float:
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"""
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Compute VAE loss function.
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L = D(x, x̂) + β · KL(q_θ(z | x) || N(0, I))
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"""
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# Reconstruction loss D(x, x̂)
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reconstruction_loss = self._reconstruction_loss(original, reconstructed)
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# KL divergence: KL(q_θ(z | x) || N(0, I))
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kl_divergence = self._kl_divergence(mu, sigma)
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# Total loss
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total_loss = reconstruction_loss + self.beta * kl_divergence
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return total_loss
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def _reconstruction_loss(self, original: str, reconstructed: str) -> float:
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"""Compute reconstruction loss D(x, x̂)"""
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# Simple character-level cross-entropy placeholder
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# In practice, would use token-level loss
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if not original or not reconstructed:
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return 0.0
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# Simplified: edit distance normalized by length
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max_len = max(len(original), len(reconstructed))
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if max_len == 0:
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return 0.0
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# Placeholder: use length difference as loss
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return abs(len(original) - len(reconstructed)) / max_len
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def _kl_divergence(self, mu: np.ndarray, sigma: np.ndarray) -> float:
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"""
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Compute KL divergence: KL(q_θ(z | x) || N(0, I))
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KL(N(μ, σ²) || N(0, I)) = -0.5 * Σ(1 + log(σ²) - μ² - σ²)
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"""
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return -0.5 * np.sum(1 + np.log(sigma**2) - mu**2 - sigma**2)
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# Singleton instance
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_neural_instance: Optional[NeuralDeltaGCLCompressor] = None
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def get_neural_compressor() -> NeuralDeltaGCLCompressor:
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"""Get singleton neural compressor instance"""
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global _neural_instance
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if _neural_instance is None:
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_neural_instance = NeuralDeltaGCLCompressor()
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return _neural_instance
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if __name__ == "__main__":
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# Test neural compression (placeholder mode)
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print("=" * 70)
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print("NEURAL DELTA GCL COMPRESSION (Placeholder)")
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print("=" * 70)
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compressor = NeuralDeltaGCLCompressor()
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test_manifest = {
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"layer": "CORE",
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"domain": "COMPUTE",
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"tier": "FOAM",
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"condition": "STABLE",
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"metadata": {"compression_ratio": 0.92, "field_phi": 0.85}
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}
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print(f"\n[1] Compressing with neural layer...")
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result = compressor.compress_with_neural(test_manifest, "neural_test_1", verify=False)
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print(f" Neural ratio: {result.neural_ratio:.4f}")
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print(f" Total ratio: {result.total_ratio:.4f}")
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print(f" Total reduction: {(1 - result.total_ratio) * 100:.2f}%")
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print(f" Verified: {result.verified}")
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if result.verification_error:
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print(f" Verification Error: {result.verification_error}")
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print(f"\n[2] Model state:")
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print(f" Trained: {compressor.is_trained}")
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print(f" Version: {compressor.model_version}")
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print(f" Latent dim: {compressor.latent_dim}")
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print("\n" + "=" * 70)
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print("NEURAL COMPRESSION PLACEHOLDER OPERATIONAL")
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print("Note: Actual neural network training required for production use")
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print("=" * 70)
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