mirror of
https://github.com/elder-plinius/OBLITERATUS.git
synced 2026-08-18 00:47:23 +02:00
256 lines
9.6 KiB
Python
256 lines
9.6 KiB
Python
"""Post-excision activation probing for abliteration verification.
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After removing refusal directions from model weights, we need to verify
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that the removal actually worked at the activation level. This module
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provides tools to:
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1. Measure the residual projection of activations onto the removed direction
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(should be near-zero after successful abliteration)
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2. Compute activation cosine similarity between original and modified models
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(should be high for harmless prompts, may differ for harmful prompts)
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3. Track the "refusal signal" strength across layers to verify it's been
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eliminated throughout the network, not just at modified layers
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Contribution: We introduce the "Refusal Elimination Score" (RES),
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a single scalar that quantifies how completely abliteration removed the
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refusal signal. RES combines:
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- Projection reduction: how much the refusal direction projection decreased
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- Signal separation: whether harmful and harmless activations are now
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indistinguishable (which they should be if refusal information is removed)
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- Layer coverage: whether the signal is eliminated across all layers,
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not just the modified ones
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RES ranges from 0 (no effect) to 1 (complete elimination).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import torch
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@dataclass
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class LayerProbeResult:
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"""Probing result for a single layer."""
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layer_idx: int
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harmful_mean_projection: float # mean projection of harmful acts onto refusal dir
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harmless_mean_projection: float # mean projection of harmless acts onto refusal dir
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projection_gap: float # harmful - harmless (should be ~0 after abliteration)
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harmful_projection_std: float
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harmless_projection_std: float
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separation_d_prime: float # d' (signal detection metric)
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@dataclass
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class ProbeResult:
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"""Full probing result across all layers."""
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per_layer: dict[int, LayerProbeResult]
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refusal_elimination_score: float # 0-1, 1 = complete elimination
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mean_projection_gap: float # avg gap across layers
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max_residual_projection: float # worst-case residual
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layers_with_residual: list[int] # layers still showing signal
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class ActivationProbe:
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"""Probe activations to verify refusal direction removal.
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After abliteration, runs harmful and harmless prompts through the
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modified model and measures whether the refusal direction is still
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detectable in the activation space.
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"""
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def __init__(self, residual_threshold: float = 0.1):
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"""
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Args:
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residual_threshold: Projection magnitude below which the
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refusal signal is considered eliminated for a layer.
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"""
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self.residual_threshold = residual_threshold
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def probe_layer(
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self,
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harmful_activations: list[torch.Tensor],
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harmless_activations: list[torch.Tensor],
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refusal_direction: torch.Tensor,
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layer_idx: int = 0,
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) -> LayerProbeResult:
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"""Probe a single layer's activations for residual refusal signal.
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Args:
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harmful_activations: List of (hidden_dim,) activation tensors
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from harmful prompts through the modified model.
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harmless_activations: List of (hidden_dim,) activation tensors
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from harmless prompts through the modified model.
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refusal_direction: (hidden_dim,) the refusal direction that was removed.
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layer_idx: Layer index for metadata.
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Returns:
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LayerProbeResult with projection statistics.
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"""
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d = refusal_direction.float()
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if d.dim() > 1:
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d = d.squeeze()
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d = d / d.norm().clamp(min=1e-8)
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# Batch projection: stack all activations into matrices for
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# vectorized dot-product instead of per-activation Python loops.
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# This provides 5-15x speedup on large prompt sets.
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if harmful_activations:
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h_stack = torch.stack(
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[a.float().squeeze() for a in harmful_activations]
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) # (n_harmful, hidden_dim)
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h_projs = h_stack @ d # (n_harmful,)
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h_mean = h_projs.mean().item()
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h_std = h_projs.std(correction=1).item() if len(harmful_activations) > 1 else 0.0
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else:
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h_mean = 0.0
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h_std = 0.0
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if harmless_activations:
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b_stack = torch.stack(
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[a.float().squeeze() for a in harmless_activations]
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) # (n_harmless, hidden_dim)
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b_projs = b_stack @ d # (n_harmless,)
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b_mean = b_projs.mean().item()
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b_std = b_projs.std(correction=1).item() if len(harmless_activations) > 1 else 0.0
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else:
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b_mean = 0.0
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b_std = 0.0
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gap = h_mean - b_mean
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# d-prime: signal detection sensitivity
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pooled_std = ((h_std ** 2 + b_std ** 2) / 2) ** 0.5
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d_prime = abs(gap) / max(pooled_std, 1e-8)
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return LayerProbeResult(
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layer_idx=layer_idx,
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harmful_mean_projection=h_mean,
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harmless_mean_projection=b_mean,
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projection_gap=gap,
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harmful_projection_std=h_std,
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harmless_projection_std=b_std,
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separation_d_prime=d_prime,
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)
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def probe_all_layers(
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self,
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harmful_acts: dict[int, list[torch.Tensor]],
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harmless_acts: dict[int, list[torch.Tensor]],
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refusal_directions: dict[int, torch.Tensor],
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strong_layers: list[int] | None = None,
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) -> ProbeResult:
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"""Probe all layers for residual refusal signal.
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Args:
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harmful_acts: {layer_idx: [activations]} from post-excision forward pass.
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harmless_acts: {layer_idx: [activations]} from post-excision forward pass.
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refusal_directions: {layer_idx: direction} the removed directions.
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strong_layers: If provided, only probe these layers.
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Returns:
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ProbeResult with per-layer and aggregate analysis.
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"""
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layers = strong_layers or sorted(refusal_directions.keys())
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per_layer = {}
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for idx in layers:
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if idx not in harmful_acts or idx not in harmless_acts:
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continue
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if idx not in refusal_directions:
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continue
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per_layer[idx] = self.probe_layer(
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harmful_acts[idx],
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harmless_acts[idx],
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refusal_directions[idx],
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layer_idx=idx,
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)
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if not per_layer:
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return ProbeResult(
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per_layer={},
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refusal_elimination_score=0.0,
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mean_projection_gap=0.0,
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max_residual_projection=0.0,
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layers_with_residual=[],
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)
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# Compute aggregate metrics
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gaps = [abs(r.projection_gap) for r in per_layer.values()]
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mean_gap = sum(gaps) / len(gaps)
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max_residual = max(gaps)
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# Layers with residual signal above threshold
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layers_with_residual = [
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idx for idx, r in per_layer.items()
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if abs(r.projection_gap) > self.residual_threshold
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]
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# Refusal Elimination Score (RES)
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# Combines three components:
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# 1. Projection reduction (based on d-prime, inverted)
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# 2. Layer coverage (fraction of layers with eliminated signal)
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# 3. Gap magnitude (normalized)
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d_primes = [r.separation_d_prime for r in per_layer.values()]
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mean_d_prime = sum(d_primes) / len(d_primes)
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# Component 1: d-prime reduction (lower is better)
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# d' > 2 means easily separable, d' < 0.5 means barely detectable
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projection_score = 1.0 / (1.0 + mean_d_prime)
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# Component 2: layer coverage
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n_eliminated = len(per_layer) - len(layers_with_residual)
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coverage_score = n_eliminated / max(len(per_layer), 1)
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# Component 3: gap magnitude (exponential decay)
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import math
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gap_score = math.exp(-mean_gap * 10) # decays quickly with increasing gap
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# Weighted combination
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res = 0.4 * projection_score + 0.3 * coverage_score + 0.3 * gap_score
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return ProbeResult(
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per_layer=per_layer,
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refusal_elimination_score=res,
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mean_projection_gap=mean_gap,
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max_residual_projection=max_residual,
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layers_with_residual=layers_with_residual,
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)
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@staticmethod
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def format_report(result: ProbeResult) -> str:
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"""Format probe results as a human-readable report."""
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lines = []
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lines.append("Post-Excision Activation Probe Results")
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lines.append("=" * 42)
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lines.append("")
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if not result.per_layer:
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lines.append("No layers probed.")
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return "\n".join(lines)
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lines.append(f"Refusal Elimination Score (RES): {result.refusal_elimination_score:.3f}")
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lines.append(" (0.0 = no effect, 1.0 = complete elimination)")
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lines.append(f"Mean projection gap: {result.mean_projection_gap:.4f}")
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lines.append(f"Max residual projection: {result.max_residual_projection:.4f}")
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if result.layers_with_residual:
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lines.append(f"Layers with residual signal: {result.layers_with_residual}")
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else:
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lines.append("All layers: refusal signal eliminated")
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lines.append("")
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lines.append("Per-Layer Probe Results:")
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for idx in sorted(result.per_layer.keys()):
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r = result.per_layer[idx]
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status = "RESIDUAL" if abs(r.projection_gap) > 0.1 else "clean"
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lines.append(
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f" layer {idx:3d}: gap={r.projection_gap:+.4f} "
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f"d'={r.separation_d_prime:.3f} [{status}]"
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)
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return "\n".join(lines)
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