diff --git a/README.md b/README.md index ec4e809..a444e60 100644 --- a/README.md +++ b/README.md @@ -127,87 +127,6 @@ save the model, upload it to Hugging Face, chat with it to test how well it work run standard benchmarks on it, or any combination of those actions. -## Research features - -In addition to its primary function of removing model censorship, Heretic also -provides features designed to support research into the semantics of model internals -(interpretability). To use those features, you need to install Heretic with the -optional `research` extra: - -```sh -pip install -U 'heretic-llm[research]' -``` - -This gives you access to the following functionality: - -### Generate plots of residual vectors by passing `--plot-residuals` - -When run with this flag, Heretic will: - -1. Compute residual vectors (hidden states) for the first output token, - for each transformer layer, for both "harmful" and "harmless" prompts. -2. Perform a [PaCMAP projection](https://github.com/YingfanWang/PaCMAP) - from residual space to 2D-space. -3. Left-right align the projections of "harmful"/"harmless" residuals - by their geometric medians to make projections for consecutive layers - more similar. Additionally, PaCMAP is initialized with the previous - layer's projections for each new layer, minimizing disruptive transitions. -4. Scatter-plot the projections, generating a PNG image for each layer. -5. Generate an animation showing how residuals transform between layers, - as an animated GIF. - -Plot of residual vectors - -See [the configuration file](config.default.toml) for options that allow you -to control various aspects of the generated plots. - -Note that PaCMAP is an expensive operation that is performed on the CPU. -For larger models, it can take an hour or more to compute projections -for all layers. - -### Print details about residual geometry by passing `--print-residual-geometry` - -If you are interested in a quantitative analysis of how residual vectors -for "harmful" and "harmless" prompts relate to each other, this flag gives you -the following table, packed with metrics that can facilitate understanding -the same (for [gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it) -in this case): - -``` -┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓ -┃ Layer ┃ S(g,b) ┃ S(g*,b*) ┃ S(g,r) ┃ S(g*,r*) ┃ S(b,r) ┃ S(b*,r*) ┃ |g| ┃ |g*| ┃ |b| ┃ |b*| ┃ |r| ┃ |r*| ┃ Silh ┃ -┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩ -│ 1 │ 1.0000 │ 1.0000 │ -0.4311 │ -0.4906 │ -0.4254 │ -0.4847 │ 170.29 │ 170.49 │ 169.78 │ 169.85 │ 1.19 │ 1.31 │ 0.0480 │ -│ 2 │ 1.0000 │ 1.0000 │ 0.4297 │ 0.4465 │ 0.4365 │ 0.4524 │ 768.55 │ 768.77 │ 771.32 │ 771.36 │ 6.39 │ 5.76 │ 0.0745 │ -│ 3 │ 0.9999 │ 1.0000 │ -0.5699 │ -0.5577 │ -0.5614 │ -0.5498 │ 1020.98 │ 1021.13 │ 1013.80 │ 1014.71 │ 12.70 │ 11.60 │ 0.0920 │ -│ 4 │ 0.9999 │ 1.0000 │ 0.6582 │ 0.6553 │ 0.6659 │ 0.6627 │ 1356.39 │ 1356.20 │ 1368.71 │ 1367.95 │ 18.62 │ 17.84 │ 0.0957 │ -│ 5 │ 0.9987 │ 0.9990 │ -0.6880 │ -0.6761 │ -0.6497 │ -0.6418 │ 766.54 │ 762.25 │ 731.75 │ 732.42 │ 51.97 │ 45.24 │ 0.1018 │ -│ 6 │ 0.9998 │ 0.9998 │ -0.1983 │ -0.2312 │ -0.1811 │ -0.2141 │ 2417.35 │ 2421.08 │ 2409.18 │ 2411.40 │ 43.06 │ 43.47 │ 0.0900 │ -│ 7 │ 0.9998 │ 0.9997 │ -0.5258 │ -0.5746 │ -0.5072 │ -0.5560 │ 3444.92 │ 3474.99 │ 3400.01 │ 3421.63 │ 86.94 │ 94.38 │ 0.0492 │ -│ 8 │ 0.9990 │ 0.9991 │ 0.8235 │ 0.8312 │ 0.8479 │ 0.8542 │ 4596.54 │ 4615.62 │ 4918.32 │ 4934.20 │ 384.87 │ 377.87 │ 0.2278 │ -│ 9 │ 0.9992 │ 0.9992 │ 0.5335 │ 0.5441 │ 0.5678 │ 0.5780 │ 5322.30 │ 5316.96 │ 5468.65 │ 5466.98 │ 265.68 │ 267.28 │ 0.1318 │ -│ 10 │ 0.9974 │ 0.9973 │ 0.8189 │ 0.8250 │ 0.8579 │ 0.8644 │ 5328.81 │ 5325.63 │ 5953.35 │ 5985.15 │ 743.95 │ 779.74 │ 0.2863 │ -│ 11 │ 0.9977 │ 0.9978 │ 0.4262 │ 0.4045 │ 0.4862 │ 0.4645 │ 9644.02 │ 9674.06 │ 9983.47 │ 9990.28 │ 743.28 │ 726.99 │ 0.1576 │ -│ 12 │ 0.9904 │ 0.9907 │ 0.4384 │ 0.4077 │ 0.5586 │ 0.5283 │ 10257.40 │ 10368.50 │ 11114.51 │ 11151.21 │ 1711.18 │ 1664.69 │ 0.1890 │ -│ 13 │ 0.9867 │ 0.9874 │ 0.4007 │ 0.3680 │ 0.5444 │ 0.5103 │ 12305.12 │ 12423.75 │ 13440.31 │ 13432.47 │ 2386.43 │ 2282.47 │ 0.1293 │ -│ 14 │ 0.9921 │ 0.9922 │ 0.3198 │ 0.2682 │ 0.4364 │ 0.3859 │ 16929.16 │ 17080.37 │ 17826.97 │ 17836.03 │ 2365.23 │ 2301.87 │ 0.1282 │ -│ 15 │ 0.9846 │ 0.9850 │ 0.1198 │ 0.0963 │ 0.2913 │ 0.2663 │ 16858.58 │ 16949.44 │ 17496.00 │ 17502.88 │ 3077.08 │ 3029.60 │ 0.1611 │ -│ 16 │ 0.9686 │ 0.9689 │ -0.0029 │ -0.0254 │ 0.2457 │ 0.2226 │ 18912.77 │ 19074.86 │ 19510.56 │ 19559.62 │ 4848.35 │ 4839.75 │ 0.1516 │ -│ 17 │ 0.9782 │ 0.9784 │ -0.0174 │ -0.0381 │ 0.1908 │ 0.1694 │ 27098.09 │ 27273.00 │ 27601.12 │ 27653.12 │ 5738.19 │ 5724.21 │ 0.1641 │ -│ 18 │ 0.9184 │ 0.9196 │ 0.1343 │ 0.1430 │ 0.5155 │ 0.5204 │ 190.16 │ 190.35 │ 219.91 │ 220.62 │ 87.82 │ 87.59 │ 0.1855 │ -└───────┴────────┴──────────┴─────────┴──────────┴─────────┴──────────┴──────────┴──────────┴──────────┴──────────┴─────────┴─────────┴────────┘ -g = mean of residual vectors for good prompts -g* = geometric median of residual vectors for good prompts -b = mean of residual vectors for bad prompts -b* = geometric median of residual vectors for bad prompts -r = residual direction for means (i.e., b - g) -r* = residual direction for geometric medians (i.e., b* - g*) -S(x,y) = cosine similarity of x and y -|x| = L2 norm of x -Silh = Mean silhouette coefficient of residuals for good/bad clusters -``` - - ## How Heretic works Heretic implements a parametrized variant of directional ablation. For each diff --git a/config.default.toml b/config.default.toml index 7bc876d..c9e79ef 100644 --- a/config.default.toml +++ b/config.default.toml @@ -71,52 +71,21 @@ chain_of_thought_skips = [ # Whether to print additional information that can help with debugging. print_debug_information = false -# Whether to print detailed information about residuals and residual directions. -print_residual_geometry = false - -# Whether to generate plots showing PaCMAP projections of residual vectors. -plot_residuals = false - -# Base path to save plots of residual vectors to. -residual_plot_path = "plots" - -# Title placed above plots of residual vectors. -residual_plot_title = 'PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts' - -# Matplotlib style sheet to use for plots of residual vectors. -residual_plot_style = "dark_background" - -# List of scorers to evaluate. -# Each entry is an object: -# { plugin = , optimization = , instance_name = } -# where is one of "minimize", "maximize", "none" (do not optimize) +# List of scorer plugin configs. Each entry is an object +# { plugin = , optimization = , instance_name = }. +# is one of "minimize", "maximize", or "none" (do not optimize). scorers = [ { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize" }, { plugin = "heretic.scorers.kl_divergence.KLDivergence", optimization = "minimize" }, ] -# Whether to adjust the residual directions so that only the component that is -# orthogonal to the good direction is subtracted during abliteration. -orthogonalize_direction = true - -# How to apply row normalization of the weights. Options: -# "none" (no normalization), -# "pre" (compute LoRA adapter relative to row-normalized weights), -# "full" (like "pre", but renormalizes to preserve original row magnitudes). -row_normalization = "full" - -# The rank of the LoRA adapter to use when "full" row normalization is used. -# Row magnitude preservation is approximate due to non-linear effects, -# and this determines the rank of that approximation. Higher ranks produce -# larger output files and may slow down evaluation. -full_normalization_lora_rank = 3 - -# The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, -# expressed as the quantile to clamp to (between 0 and 1). Disabled by default. -# This can tame so-called "massive activations" that occur in some models. -# Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values -# of the components, then clamps the magnitudes of all components to that quantile. -winsorization_quantile = 1.0 +# List of modifier plugin configs. Each entry is an object +# { plugin = , instance_name = }. +# Note that only a single modifier can currently be applied, +# and this list must contain exactly one entry. +modifiers = [ + { plugin = "heretic.modifiers.abliteration.Abliteration" }, +] # Number of abliteration trials to run during optimization. n_trials = 200 @@ -133,29 +102,33 @@ max_shard_size = "5GB" # System prompt to use when prompting the model. system_prompt = "You are a helpful assistant." +# Plugin-specific settings live in top-level TOML tables. +# +# For scorer plugins, use: `[scorer.]` (and optionally `[scorer._]` for instance-related config). +# For modifier plugins, use: `[modifier.]` (and optionally `[modifier._]` for instance-related config). +# +# You can load multiple instances of the same plugin class by setting `instance_name` +# in the `scorers/modifiers = [...]` list. Each instance is still identified as `ClassName.instanceName` +# internally, but its config overrides live under `[scorer/modifier.ClassName_]`. +# +# Example: +# scorers = [ +# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize", instance_name = "small" }, +# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize", instance_name = "tiny" }, +# ] +# +# Shared defaults for all instances live under `[scorer.KeywordRate]` and can be overridden per +# instance under `[scorer.KeywordRate_]`. +# +# Example instance override: +# [scorer.KeywordRate_small.prompts] +# split = "test[:10]" +# # Each "dataset" below can be a Hugging Face dataset ID, a path to a dataset on disk, # or a path to a plain text file with one prompt per line (empty lines are ignored). # For text files, "column" is ignored and "split" is optional; when given, it selects # a subset of the lines using slice notation (e.g. "[:400]"). -# Dataset of prompts that tend to not result in refusals (used for calculating residual directions). -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -split = "train[:400]" -column = "text" -residual_plot_label = '"Harmless" prompts' -residual_plot_color = "royalblue" - -# Dataset of prompts that tend to result in refusals (used for calculating residual directions). -[bad_prompts] -dataset = "mlabonne/harmful_behaviors" -split = "train[:400]" -column = "text" -residual_plot_label = '"Harmful" prompts' -residual_plot_color = "darkorange" - -# Plugin-specific settings live in a top-level TOML table. -# For scorer plugins, use: `[scorer.]` (and optionally `[scorer._]` for instance-related config). [scorer.KeywordRate] # Name that describes what the configured keyword rate measures. score_name = "Refusals" @@ -200,30 +173,50 @@ keyword_markers = [ "ethical boundaries", ] -# Scorer-owned evaluation prompts +# Dataset of prompts to evaluate the keyword match rate on. [scorer.KeywordRate.prompts] dataset = "mlabonne/harmful_behaviors" split = "test[:100]" column = "text" -# You can also load multiple instances of the same scorer class by setting `instance_name` -# in the `scorers = [...]` list. Each instance is still identified as `ClassName.instanceName` -# internally, but its config overrides live under `[scorer.ClassName_]`. -# -# Example: -# scorers = [ -# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = 'minimize', instance_name = "small" }, -# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = 'minimize', instance_name = "tiny" }, -# ] -# -# Shared defaults for all instances live under `[scorer.KeywordRate]` and can be overridden per -# instance under `[scorer.KeywordRate_]`. -# -# Example instance override: -# [scorer.KeywordRate_small.prompts] -# split = "test[:10]" - +# Dataset of prompts used to measure KL divergence from original model. [scorer.KLDivergence.prompts] dataset = "mlabonne/harmless_alpaca" split = "test[:100]" column = "text" + +[modifier.Abliteration] +# Whether to adjust the residual directions so that only the component that is +# orthogonal to the good direction is subtracted during abliteration. +orthogonalize_direction = true + +# How to apply row normalization of the weights. Options: +# "none" (no normalization), +# "pre" (compute LoRA adapter relative to row-normalized weights), +# "full" (like "pre", but renormalizes to preserve original row magnitudes). +row_normalization = "full" + +# The rank of the LoRA adapter to use when "full" row normalization is used. +# Row magnitude preservation is approximate due to non-linear effects, +# and this determines the rank of that approximation. Higher ranks produce +# larger output files and may slow down evaluation. +full_normalization_lora_rank = 3 + +# The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, +# expressed as the quantile to clamp to (between 0 and 1). Disabled by default. +# This can tame so-called "massive activations" that occur in some models. +# Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values +# of the components, then clamps the magnitudes of all components to that quantile. +winsorization_quantile = 1.0 + +# Dataset of prompts that tend to produce desirable responses. +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +split = "train[:400]" +column = "text" + +# Dataset of prompts that tend to produce undesirable responses. +[modifier.Abliteration.bad_prompts] +dataset = "mlabonne/harmful_behaviors" +split = "train[:400]" +column = "text" diff --git a/config.nohumor.toml b/config.nohumor.toml index 7632f1b..5628b09 100644 --- a/config.nohumor.toml +++ b/config.nohumor.toml @@ -3,22 +3,6 @@ max_response_length = 300 -residual_plot_title = "PaCMAP Projection of Residuals for Serious/Humorous Prompts" - -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -split = "train[:400]" -column = "text" -residual_plot_label = "Serious prompts" -residual_plot_color = "royalblue" - -[bad_prompts] -dataset = "UnstableLlama/jokes" -split = "train[:200]" -column = "text" -residual_plot_label = "Humorous prompts" -residual_plot_color = "darkorange" - [scorer.KeywordRate] score_name = "Responses with humor" @@ -70,3 +54,13 @@ column = "text" dataset = "mlabonne/harmless_alpaca" split = "test[:100]" column = "text" + +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +split = "train[:400]" +column = "text" + +[modifier.Abliteration.bad_prompts] +dataset = "UnstableLlama/jokes" +split = "train[:200]" +column = "text" diff --git a/config.noslop.toml b/config.noslop.toml index 5e8437d..8a39754 100644 --- a/config.noslop.toml +++ b/config.noslop.toml @@ -3,26 +3,8 @@ max_response_length = 300 -residual_plot_title = "PaCMAP Projection of Residuals for Slop-Suppressing/Inducing Prompts" - system_prompt = "You are a professional writer." -[good_prompts] -dataset = "llm-aes/writing-prompts" -split = "train[:500]" -column = "prompt" -prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:" -residual_plot_label = "Slop-suppressing prompts" -residual_plot_color = "royalblue" - -[bad_prompts] -dataset = "llm-aes/writing-prompts" -split = "train[:500]" -column = "prompt" -prefix = "Write a short story based on the writing prompt below. Make extensive use of literary cliches, purple prose, and flowery language.\n\nWriting prompt:" -residual_plot_label = "Slop-inducing prompts" -residual_plot_color = "darkorange" - [scorer.KeywordRate] score_name = "Responses with slop" @@ -164,3 +146,15 @@ dataset = "llm-aes/writing-prompts" split = "train[1000:1100]" column = "prompt" prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:" + +[modifier.Abliteration.good_prompts] +dataset = "llm-aes/writing-prompts" +split = "train[:500]" +column = "prompt" +prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:" + +[modifier.Abliteration.bad_prompts] +dataset = "llm-aes/writing-prompts" +split = "train[:500]" +column = "prompt" +prefix = "Write a short story based on the writing prompt below. Make extensive use of literary cliches, purple prose, and flowery language.\n\nWriting prompt:" diff --git a/pyproject.toml b/pyproject.toml index ddd422f..a4c594e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,15 +44,6 @@ dependencies = [ "transformers[kernels]~=5.6", ] -[project.optional-dependencies] -research = [ - "geom-median~=0.1", - "imageio~=2.37", - "matplotlib~=3.10", - "pacmap~=0.8", - "scikit-learn~=1.7", -] - [dependency-groups] dev = [ "ruff>=0.14.5", diff --git a/src/heretic/analyzer.py b/src/heretic/analyzer.py deleted file mode 100644 index 4d88424..0000000 --- a/src/heretic/analyzer.py +++ /dev/null @@ -1,357 +0,0 @@ -# SPDX-License-Identifier: AGPL-3.0-or-later -# Copyright (C) 2025-2026 Philipp Emanuel Weidmann + contributors - -from pathlib import Path - -import numpy as np -import torch -import torch.linalg as LA -import torch.nn.functional as F -from numpy.typing import NDArray -from rich.progress import track -from rich.table import Table -from torch import Tensor - -from .config import Settings -from .model import Model -from .utils import print - - -class Analyzer: - def __init__( - self, - settings: Settings, - model: Model, - good_residuals: Tensor, - bad_residuals: Tensor, - ): - self.settings = settings - self.model = model - self.good_residuals = good_residuals - self.bad_residuals = bad_residuals - - def print_residual_geometry(self): - try: - from geom_median.torch import ( # ty:ignore[unresolved-import] - compute_geometric_median, - ) - from sklearn.metrics import silhouette_score # ty:ignore[unresolved-import] - except ImportError: - print() - print( - ( - "[red]Research dependencies not found. Printing residual geometry requires " - "installing Heretic with the optional research feature, i.e., " - "using \"pip install -U 'heretic-llm\\[research]'\".[/]" - ) - ) - return - - print() - print("Computing residual geometry...") - - table = Table() - table.add_column("Layer", justify="right") - table.add_column("S(g,b)", justify="right") - table.add_column("S(g*,b*)", justify="right") - table.add_column("S(g,r)", justify="right") - table.add_column("S(g*,r*)", justify="right") - table.add_column("S(b,r)", justify="right") - table.add_column("S(b*,r*)", justify="right") - table.add_column("|g|", justify="right") - table.add_column("|g*|", justify="right") - table.add_column("|b|", justify="right") - table.add_column("|b*|", justify="right") - table.add_column("|r|", justify="right") - table.add_column("|r*|", justify="right") - table.add_column("Silh", justify="right") - - g = self.good_residuals.mean(dim=0) - g_star = torch.stack( - [ - compute_geometric_median( - self.good_residuals[:, layer_index, :].detach().cpu() - ).median - for layer_index in range(len(self.model.get_layers()) + 1) - ] - ) - b = self.bad_residuals.mean(dim=0) - b_star = torch.stack( - [ - compute_geometric_median( - self.bad_residuals[:, layer_index, :].detach().cpu() - ).median - for layer_index in range(len(self.model.get_layers()) + 1) - ] - ) - r = b - g - r_star = b_star - g_star - - g_b_similarities = F.cosine_similarity(g, b, dim=-1) - g_star_b_star_similarities = F.cosine_similarity(g_star, b_star, dim=-1) - g_r_similarities = F.cosine_similarity(g, r, dim=-1) - g_star_r_star_similarities = F.cosine_similarity(g_star, r_star, dim=-1) - b_r_similarities = F.cosine_similarity(b, r, dim=-1) - b_star_r_star_similarities = F.cosine_similarity(b_star, r_star, dim=-1) - - g_norms = LA.vector_norm(g, dim=-1) - g_star_norms = LA.vector_norm(g_star, dim=-1) - b_norms = LA.vector_norm(b, dim=-1) - b_star_norms = LA.vector_norm(b_star, dim=-1) - r_norms = LA.vector_norm(r, dim=-1) - r_star_norms = LA.vector_norm(r_star, dim=-1) - - residuals = ( - torch.cat( - [ - self.good_residuals, - self.bad_residuals, - ], - dim=0, - ) - .detach() - .cpu() - .numpy() - ) - labels = [0] * len(self.good_residuals) + [1] * len(self.bad_residuals) - silhouettes = [ - silhouette_score(residuals[:, layer_index, :], labels) - for layer_index in range(len(self.model.get_layers()) + 1) - ] - - for layer_index in range(1, len(self.model.get_layers()) + 1): - table.add_row( - f"{layer_index}", - f"{g_b_similarities[layer_index].item():.4f}", - f"{g_star_b_star_similarities[layer_index].item():.4f}", - f"{g_r_similarities[layer_index].item():.4f}", - f"{g_star_r_star_similarities[layer_index].item():.4f}", - f"{b_r_similarities[layer_index].item():.4f}", - f"{b_star_r_star_similarities[layer_index].item():.4f}", - f"{g_norms[layer_index].item():.2f}", - f"{g_star_norms[layer_index].item():.2f}", - f"{b_norms[layer_index].item():.2f}", - f"{b_star_norms[layer_index].item():.2f}", - f"{r_norms[layer_index].item():.2f}", - f"{r_star_norms[layer_index].item():.2f}", - f"{silhouettes[layer_index]:.4f}", - ) - - print() - print("[bold]Residual Geometry[/]") - print(table) - print("[bold]g[/] = mean of residual vectors for good prompts") - print("[bold]g*[/] = geometric median of residual vectors for good prompts") - print("[bold]b[/] = mean of residual vectors for bad prompts") - print("[bold]b*[/] = geometric median of residual vectors for bad prompts") - print("[bold]r[/] = residual direction for means (i.e., [bold]b - g[/])") - print( - "[bold]r*[/] = residual direction for geometric medians (i.e., [bold]b* - g*[/])" - ) - print("[bold]S(x,y)[/] = cosine similarity of [bold]x[/] and [bold]y[/]") - print("[bold]|x|[/] = L2 norm of [bold]x[/]") - print( - "[bold]Silh[/] = Mean silhouette coefficient of residuals for good/bad clusters" - ) - - def plot_residuals(self): - try: - import imageio.v3 as iio # ty:ignore[unresolved-import] - import matplotlib.pyplot as plt # ty:ignore[unresolved-import] - from geom_median.numpy import ( # ty:ignore[unresolved-import] - compute_geometric_median, - ) - from pacmap import PaCMAP # ty:ignore[unresolved-import] - except ImportError: - print() - print( - ( - "[red]Research dependencies not found. Plotting residuals requires " - "installing Heretic with the optional research feature, i.e., " - "using \"pip install -U 'heretic-llm\\[research]'\".[/]" - ) - ) - return - - LAYER_FRAME_DURATION = 1000 - N_TRANSITION_FRAMES = 20 - TRANSITION_FRAME_DURATION = 50 - - print() - print("Plotting residual vectors...") - - layer_residuals_2d = [] - pacmap_init = None - - for layer_index in track( - range(1, len(self.model.get_layers()) + 1), - description="* Computing PaCMAP projections...", - ): - good_residuals = ( - self.good_residuals[:, layer_index, :].detach().cpu().numpy() - ) - bad_residuals = self.bad_residuals[:, layer_index, :].detach().cpu().numpy() - - residuals = np.vstack((good_residuals, bad_residuals)) - embedding = PaCMAP(n_components=2, n_neighbors=30) - residuals_2d = embedding.fit_transform(residuals, init=pacmap_init) - pacmap_init = residuals_2d - - n_good_residuals = good_residuals.shape[0] - good_residuals_2d = residuals_2d[:n_good_residuals] - bad_residuals_2d = residuals_2d[n_good_residuals:] - - # Important: These are the medians of the 2D-projected residuals, - # not the projections of the medians of the residuals. - # Their only purpose is to rotate the individual plots - # into a consistent orientation. They are not suitable - # for being plotted themselves. - good_anchor = compute_geometric_median(good_residuals_2d).median - bad_anchor = compute_geometric_median(bad_residuals_2d).median - - # Rotate points to make the line connecting the medians horizontal, - # with the median of the good residuals on the left. - direction = bad_anchor - good_anchor - angle = -np.arctan2(direction[1], direction[0]) - cosine = np.cos(angle) - sine = np.sin(angle) - rotation_matrix = np.array([[cosine, -sine], [sine, cosine]]) - residuals_2d = residuals_2d @ rotation_matrix.T - - good_residuals_2d = residuals_2d[:n_good_residuals] - bad_residuals_2d = residuals_2d[n_good_residuals:] - - layer_residuals_2d.append((good_residuals_2d, bad_residuals_2d)) - - plt.style.use(self.settings.residual_plot_style) - - def plot( - image_path: Path, - layer_index: int, - good_residuals_2d: NDArray, - bad_residuals_2d: NDArray, - ): - fig, ax = plt.subplots(figsize=(8, 6)) - - ax.scatter( - good_residuals_2d[:, 0], - good_residuals_2d[:, 1], - s=10, - c=self.settings.good_prompts.residual_plot_color, - alpha=0.5, - label=self.settings.good_prompts.residual_plot_label, - ) - ax.scatter( - bad_residuals_2d[:, 0], - bad_residuals_2d[:, 1], - s=10, - c=self.settings.bad_prompts.residual_plot_color, - alpha=0.5, - label=self.settings.bad_prompts.residual_plot_label, - ) - - ax.set_title(self.settings.residual_plot_title, pad=11) - ax.legend(loc="upper right") - ax.grid(False) - ax.set_xticks([]) - ax.set_yticks([]) - - fig.text( - 0.018, - 0.02, - self.settings.model, - ha="left", - va="bottom", - fontsize=12, - ) - fig.text( - 0.982, - 0.02, - f"Layer {layer_index:03}", - ha="right", - va="bottom", - fontsize=12, - ) - - fig.tight_layout() - fig.subplots_adjust(bottom=0.08) - - fig.savefig(image_path, dpi=100) - plt.close(fig) - - base_path = Path( - self.settings.residual_plot_path - ) / self.settings.model.replace( - "/", - "_", - ).replace( - "\\", - "_", - ) - - base_path.mkdir(parents=True, exist_ok=True) - - images = [] - durations = [] - - for layer_index, ( - good_residuals_2d, - bad_residuals_2d, - ) in enumerate( - track( - layer_residuals_2d, - description="* Generating plots...", - ), - 1, - ): - image_path = base_path / f"layer_{layer_index:03}.png" - - plot(image_path, layer_index, good_residuals_2d, bad_residuals_2d) - - images.append(iio.imread(image_path)) - durations.append(LAYER_FRAME_DURATION) - - if layer_index < len(layer_residuals_2d): - # The first frame of the transition is the layer frame created above. - # The last frame is the next layer frame, created in the next iteration of the outer loop. - # The following are the intermediate frames. - # There are a total of N_TRANSITION_FRAMES frame changes in the transition. - for frame_index in range(1, N_TRANSITION_FRAMES): - image_path = ( - base_path / f"layer_{layer_index:03}_frame_{frame_index:03}.png" - ) - - progress = frame_index / N_TRANSITION_FRAMES - - good_residuals_2d_interpolated = good_residuals_2d + progress * ( - layer_residuals_2d[layer_index][0] - good_residuals_2d - ) - bad_residuals_2d_interpolated = bad_residuals_2d + progress * ( - layer_residuals_2d[layer_index][1] - bad_residuals_2d - ) - - plot( - image_path, - layer_index, - good_residuals_2d_interpolated, - bad_residuals_2d_interpolated, - ) - - images.append(iio.imread(image_path)) - durations.append(TRANSITION_FRAME_DURATION) - - # Delete the image file containing the animation frame. - # We have already read its contents and it serves no purpose - # other than building the animation. - image_path.unlink() - - print("* Generating animation...") - - iio.imwrite( - base_path / "animation.gif", - images, - duration=durations, - loop=0, - ) - - print(f"* Plots saved to [bold]{base_path.resolve()}[/].") diff --git a/src/heretic/config.py b/src/heretic/config.py index a119b24..d0a519a 100644 --- a/src/heretic/config.py +++ b/src/heretic/config.py @@ -32,13 +32,6 @@ class QuantizationMethod(str, Enum): BNB_4BIT = "bnb_4bit" -class RowNormalization(str, Enum): - NONE = "none" - PRE = "pre" - # POST = "post" # Theoretically possible, but provides no advantage. - FULL = "full" - - class ExportStrategy(str, Enum): MERGE = "merge" ADAPTER = "adapter" @@ -79,18 +72,6 @@ class DatasetSpecification(BaseModel): description="System prompt to use with the prompts (overrides global system prompt if set).", ) - residual_plot_label: str | None = Field( - default=None, - description="Label to use for the dataset in plots of residual vectors.", - exclude=True, - ) - - residual_plot_color: str | None = Field( - default=None, - description="Matplotlib color to use for the dataset in plots of residual vectors.", - exclude=True, - ) - class ScorerConfig(BaseModel): """ @@ -142,6 +123,48 @@ class ScorerConfig(BaseModel): return value +class ModifierConfig(BaseModel): + """ + Configuration for a modifier plugin. + + TOML format: + - { plugin = "", instance_name = "" } + """ + + plugin: str = Field( + description=( + "Plugin to load. Either a file path with class name " + "(`path/to/plugin.py:ClassName`) or a fully-qualified import path " + "(`module.submodule.ClassName`)." + ), + ) + + instance_name: str | None = Field( + default=None, + description=( + "Optional name to distinguish multiple instances of the same plugin class. " + "Instance-specific settings live under `[modifier._]`." + ), + ) + + @field_validator("instance_name") + @classmethod + def validate_instance_name(cls, value: str | None) -> str | None: + if value is None: + return value + + if not value.strip(): + raise ValueError("cannot be empty or whitespace") + + if "." in value: + raise ValueError("'.' is not allowed") + + if any(char.isspace() for char in value): + raise ValueError("whitespace is not allowed") + + return value + + class BenchmarkSpecification(BaseModel): task: str = Field( description="Task ID of the benchmark in the Language Model Evaluation Harness." @@ -304,38 +327,8 @@ class Settings(BaseSettings): exclude=True, ) - print_residual_geometry: bool = Field( - default=False, - description="Whether to print detailed information about residuals and residual directions.", - exclude=True, - ) - - plot_residuals: bool = Field( - default=False, - description="Whether to generate plots showing PaCMAP projections of residual vectors.", - exclude=True, - ) - - residual_plot_path: str = Field( - default="plots", - description="Base path to save plots of residual vectors to.", - exclude=True, - ) - - residual_plot_title: str = Field( - default='PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts', - description="Title placed above plots of residual vectors.", - exclude=True, - ) - - residual_plot_style: str = Field( - default="dark_background", - description="Matplotlib style sheet to use for plots of residual vectors.", - exclude=True, - ) - scorers: list[ScorerConfig] = Field( - default_factory=lambda: [ + default=[ ScorerConfig( plugin="heretic.scorers.keyword_rate.KeywordRate", optimization="minimize", @@ -346,48 +339,23 @@ class Settings(BaseSettings): ), ], description=( - "List of scorer plugin configs. Each entry is an object" - " { plugin = , optimization = , instance_name = }." - " is one of 'minimize', 'maximize', 'none' (do not optimize)." + "List of scorer plugin configs. Each entry is an object " + "{ plugin = , optimization = , instance_name = }. " + ' is one of "minimize", "maximize", or "none" (do not optimize).' ), ) - orthogonalize_direction: bool = Field( - default=True, + modifiers: list[ModifierConfig] = Field( + default=[ + ModifierConfig( + plugin="heretic.modifiers.abliteration.Abliteration", + ), + ], description=( - "Whether to adjust the residual directions so that only the component that is " - "orthogonal to the good direction is subtracted during abliteration." - ), - ) - - row_normalization: RowNormalization = Field( - default=RowNormalization.FULL, - description=( - "How to apply row normalization of the weights. Options: " - '"none" (no normalization), ' - '"pre" (compute LoRA adapter relative to row-normalized weights), ' - '"full" (like "pre", but renormalizes to preserve original row magnitudes).' - ), - ) - - full_normalization_lora_rank: PositiveInt = Field( - default=3, - description=( - 'The rank of the LoRA adapter to use when "full" row normalization is used. ' - "Row magnitude preservation is approximate due to non-linear effects, " - "and this determines the rank of that approximation. Higher ranks produce " - "larger output files and may slow down evaluation." - ), - ) - - winsorization_quantile: float = Field( - default=1.0, - description=( - "The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, " - "expressed as the quantile to clamp to (between 0 and 1). Disabled by default. " - 'This can tame so-called "massive activations" that occur in some models. ' - "Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values " - "of the components, then clamps the magnitudes of all components to that quantile." + "List of modifier plugin configs. Each entry is an object " + "{ plugin = , instance_name = }. " + "Note that only a single modifier can currently be applied, " + "and this list must contain exactly one entry." ), ) @@ -539,31 +507,9 @@ class Settings(BaseSettings): description="System prompt to use when prompting the model.", ) - good_prompts: DatasetSpecification = Field( - default=DatasetSpecification( - dataset="mlabonne/harmless_alpaca", - split="train[:400]", - column="text", - residual_plot_label='"Harmless" prompts', - residual_plot_color="royalblue", - ), - description="Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).", - ) - - bad_prompts: DatasetSpecification = Field( - default=DatasetSpecification( - dataset="mlabonne/harmful_behaviors", - split="train[:400]", - column="text", - residual_plot_label='"Harmful" prompts', - residual_plot_color="darkorange", - ), - description="Dataset of prompts that tend to result in refusals (used for calculating refusal directions).", - ) - # We intentionally allow extra keys so users can provide plugin-specific # configuration in TOML tables like `[scorer.KeywordRate]` which are later - # consumed via `settings.model_extra` (see `Evaluator._get_plugin_namespace`). + # consumed via `settings.model_extra` (see `plugin.get_plugin_namespace`). model_config = SettingsConfigDict(extra="allow") @classmethod diff --git a/src/heretic/evaluator.py b/src/heretic/evaluator.py index cde1224..bd11b1e 100644 --- a/src/heretic/evaluator.py +++ b/src/heretic/evaluator.py @@ -9,9 +9,9 @@ from pydantic import BaseModel from .config import DatasetSpecification, ScorerConfig, Settings from .model import Model -from .plugin import get_plugin_namespace, is_builtin_plugin, load_plugin -from .scorer import Context, Score, Scorer -from .utils import deep_merge_dicts, parse_study_direction, print +from .plugin import Context, is_builtin_plugin, load_plugin +from .scorer import Score, Scorer +from .utils import parse_study_direction, print @dataclass @@ -63,14 +63,16 @@ class Evaluator: scorer_cls.validate_contract() print( - f"* Loaded: [bold]{scorer_cls.__name__} {'- ' + config.instance_name if config.instance_name else ''}[/bold]" + f"* Loaded: [bold]{scorer_cls.__name__}{' - ' + config.instance_name if config.instance_name else ''}[/bold]" ) # Instantiate scorers. instance_name = config.instance_name or None - raw_settings = self._get_scorer_settings_raw( - scorer_cls=scorer_cls, instance_name=instance_name + raw_settings = scorer_cls.get_settings_raw( + self.settings.model_extra, + "scorer", + instance_name, ) scorer_settings: BaseModel | None = scorer_cls.validate_settings( raw_settings @@ -117,45 +119,9 @@ class Evaluator: """ specifications = [] for entry in self._scorer_entries: - if entry.scorer.settings is None: - continue - for value in dict(entry.scorer.settings).values(): - if isinstance(value, DatasetSpecification): - specifications.append(value) + specifications.extend(entry.scorer.get_dataset_specifications()) return specifications - def _get_scorer_settings_raw( - self, *, scorer_cls: type[Scorer], instance_name: str | None - ) -> dict[str, Any]: - """ - Build the raw settings dict for a scorer class and optional instance. - - Config rules: - - Base settings live in `[scorer.ClassName]` (applies to all instances). - - Instance overrides live in `[scorer.ClassName_]` (preferred). - - Only merge/validate keys that exist in the scorer Settings schema. - """ - settings_model = scorer_cls.get_settings_model() - if settings_model is None: - # No settings schema: nothing to merge/validate. - return {} - - class_name = scorer_cls.__name__ - - namespaces = [f"scorer.{class_name}"] - if instance_name: - namespaces.append(f"scorer.{class_name}_{instance_name}") - - merged_settings: dict[str, Any] = {} - allowed_keys = set(settings_model.model_fields.keys()) - - for namespace in namespaces: - raw_table = get_plugin_namespace(self.settings.model_extra, namespace) - filtered = {k: v for k, v in raw_table.items() if k in allowed_keys} - merged_settings = deep_merge_dicts(merged_settings, filtered) - - return merged_settings - def all_scorers_reproducible(self) -> bool: """ Returns True if all scorers are reproducible, diff --git a/src/heretic/main.py b/src/heretic/main.py index 35424c9..3c64fcf 100644 --- a/src/heretic/main.py +++ b/src/heretic/main.py @@ -41,7 +41,6 @@ import os import random import time import warnings -from dataclasses import asdict from importlib.metadata import version from os.path import commonprefix from pathlib import Path @@ -53,7 +52,6 @@ import numpy as np import optuna import questionary import torch -import torch.nn.functional as F import transformers from huggingface_hub import HfApi, ModelCard, ModelCardData from lm_eval.models.huggingface import HFLM @@ -69,10 +67,15 @@ from rich.table import Table from rich.text import Text from rich.traceback import install -from .analyzer import Analyzer -from .config import ExportStrategy, QuantizationMethod +from .config import ( + DatasetSpecification, + ExportStrategy, + QuantizationMethod, +) from .evaluator import Evaluator -from .model import AbliterationParameters, Model, get_model_class +from .model import Model, get_model_class +from .modifier import load_and_init_modifiers +from .plugin import Context, is_builtin_plugin from .reproduce import ( check_environment, collect_reproducibles, @@ -85,7 +88,6 @@ from .utils import ( format_exception, get_file_sha256, get_readme_intro, - get_trial_parameters, is_hf_path, load_prompts, print, @@ -243,16 +245,12 @@ def run(): # FIXME: "Reproduction"/"reproducibility" name inconsistency! reproduction_information = load_reproduction_information(settings.reproduce) - # Version 3 is the plugin-era schema, which stores generic scorer - # `scores`/`baseline_scores`. It is intentionally NOT compatible with the - # pre-plugin v1/v2 schema (hardcoded refusals/KL `metrics`), so those are - # rejected rather than silently failing on a missing key later. - if reproduction_information["version"] != "3": + if reproduction_information["version"] != "4": print( ( f"[red]Unsupported file format version: [bold]{reproduction_information['version']}[/].[/] " - "This version of Heretic reads version 3 (plugin scorer) reproduce.json files. " - "Older files were produced before the scorer-plugin refactor and are not supported. " + "This version of Heretic reads version 4 (plugin-based) reproduce.json files. " + "Older files were produced before the introduction of the plugin system and are not supported. " "Please install Heretic 1.4 to use these files." ) ) @@ -412,14 +410,27 @@ def run(): print() print_memory_usage() + # TODO: Introduce a dedicated dataset setting for test prompts. + good_prompts_dataset = DatasetSpecification( + dataset="mlabonne/harmless_alpaca", + split="train[:400]", + column="text", + ) + + bad_prompts_dataset = DatasetSpecification( + dataset="mlabonne/harmful_behaviors", + split="train[:400]", + column="text", + ) + print() - print(f"Loading good prompts from [bold]{settings.good_prompts.dataset}[/]...") - good_prompts = load_prompts(settings, settings.good_prompts) + print(f"Loading good prompts from [bold]{good_prompts_dataset.dataset}[/]...") + good_prompts = load_prompts(settings, good_prompts_dataset) print(f"* [bold]{len(good_prompts)}[/] prompts loaded") print() - print(f"Loading bad prompts from [bold]{settings.bad_prompts.dataset}[/]...") - bad_prompts = load_prompts(settings, settings.bad_prompts) + print(f"Loading bad prompts from [bold]{bad_prompts_dataset.dataset}[/]...") + bad_prompts = load_prompts(settings, bad_prompts_dataset) print(f"* [bold]{len(bad_prompts)}[/] prompts loaded") if settings.batch_size == 0: @@ -534,53 +545,17 @@ def run(): return print() - print("Calculating per-layer residual directions...") + print("Loading and initializing modifiers...") + modifier_entries = load_and_init_modifiers(settings, model) - needs_full_residuals = settings.print_residual_geometry or settings.plot_residuals - - if needs_full_residuals: - print("* Obtaining residuals for good prompts...") - good_residuals = model.get_residuals_batched(good_prompts) - print("* Obtaining residuals for bad prompts...") - bad_residuals = model.get_residuals_batched(bad_prompts) - - good_means = good_residuals.mean(dim=0) - bad_means = bad_residuals.mean(dim=0) - - analyzer = Analyzer(settings, model, good_residuals, bad_residuals) - - if settings.print_residual_geometry: - analyzer.print_residual_geometry() - - if settings.plot_residuals: - analyzer.plot_residuals() - - # We don't need the full residuals after computing their means and analyzing geometry. - del good_residuals, bad_residuals, analyzer - else: - print("* Obtaining residual mean for good prompts...") - good_means = model.get_residuals_mean(good_prompts) - print("* Obtaining residual mean for bad prompts...") - bad_means = model.get_residuals_mean(bad_prompts) - - residual_directions = F.normalize(bad_means - good_means, p=2, dim=1) - - if settings.orthogonalize_direction: - # Implements https://huggingface.co/blog/grimjim/projected-abliteration - # Adjust the residual directions so that only the component that is - # orthogonal to the good direction is subtracted during abliteration. - good_directions = F.normalize(good_means, p=2, dim=1) - projection_vector = torch.sum(residual_directions * good_directions, dim=1) - residual_directions = ( - residual_directions - projection_vector.unsqueeze(1) * good_directions - ) - residual_directions = F.normalize(residual_directions, p=2, dim=1) - del good_directions, projection_vector - - del good_means, bad_means + # `load_and_init_modifiers` currently guarantees that the returned list has exactly one element. + # This may change in the future when support for multiple modifiers is implemented. + modifier_entry = modifier_entries[0] + modifier = modifier_entry.modifier + modifier_name = modifier_entry.name # Clear cache before starting the optimization study. - # This should free up memory from the objects released with the del statements above. + # This should free up memory from temporary objects created while initializing modifiers. empty_cache() trial_index = 0 @@ -592,95 +567,21 @@ def run(): trial_index += 1 trial.set_user_attr("index", trial_index) - direction_scope = trial.suggest_categorical( - "direction_scope", - [ - "global", - "per layer", - ], - ) - - last_layer_index = len(model.get_layers()) - 1 - - # Discrimination between "harmful" and "harmless" inputs is usually strongest - # in layers slightly past the midpoint of the layer stack. See the original - # abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis. - # - # Note that we always sample this parameter even though we only need it for - # the "global" direction scope. The reason is that multivariate TPE doesn't - # work with conditional or variable-range parameters. - direction_index = trial.suggest_float( - "direction_index", - 0.4 * last_layer_index, - 0.9 * last_layer_index, - ) - - if direction_scope == "per layer": - direction_index = None - - parameters = {} - - for component in model.get_abliterable_components(): - # The parameter ranges are based on experiments with various models - # and much wider ranges. They are not set in stone and might have to be - # adjusted for future models. - # - # The MLP gets a negative lower bound that is then clamped to 0, so the - # optimizer can fully disable its ablation. The clamp puts a positive - # probability mass on exactly 0 (the continuous sampler would otherwise - # reach 0 with probability zero). Ablating the MLP is often unnecessary for - # removing refusals and tends to damage model intelligence more than - # ablating the attention output, so on many models the optimum is to leave - # it (mostly) untouched. See issue #202. - max_weight_lower_bound = -0.25 if component == "mlp.down_proj" else 0.8 - max_weight = max( - 0.0, - trial.suggest_float( - f"{component}.max_weight", - max_weight_lower_bound, - 1.5, - ), - ) - max_weight_position = trial.suggest_float( - f"{component}.max_weight_position", - 0.6 * last_layer_index, - 1.0 * last_layer_index, - ) - # For sampling purposes, min_weight is expressed as a fraction of max_weight, - # again because multivariate TPE doesn't support variable-range parameters. - # The value is transformed into the actual min_weight value below. - min_weight = trial.suggest_float( - f"{component}.min_weight", - 0.0, - 1.0, - ) - min_weight_distance = trial.suggest_float( - f"{component}.min_weight_distance", - 1.0, - max(0.6 * last_layer_index, 1.0), - ) - - parameters[component] = AbliterationParameters( - max_weight=max_weight, - max_weight_position=max_weight_position, - min_weight=(min_weight * max_weight), - min_weight_distance=min_weight_distance, - ) - - trial.set_user_attr("direction_index", direction_index) - trial.set_user_attr("parameters", {k: asdict(v) for k, v in parameters.items()}) + ctx = Context(settings=settings, model=model) + parameters = modifier.suggest_parameters(ctx, trial) + trial.set_user_attr("parameters", parameters.to_dict()) print() print( f"[magenta]Running trial [bold]{trial_index}[/] of [bold]{settings.n_trials}[/]...[/]" ) print("* Parameters:") - for name, value in get_trial_parameters(trial).items(): + for name, value in modifier.render_trial_parameters(trial).items(): print(f" * {name} = [bold]{value}[/]") print("* Resetting model...") - model.reset_model() - print("* Abliterating...") - model.abliterate(residual_directions, direction_index, parameters) + modifier.reset_model(ctx) + print(f"* Modifying model ({modifier_name})...") + modifier.modify_model(ctx, parameters) print("* Evaluating...") scores = evaluator.get_scores() objective_values = evaluator.get_objective_values(scores) @@ -834,13 +735,10 @@ def run(): trial_loop_active = False if reproduction_mode: - parameters = reproduction_information["parameters"] - trial = create_trial( values=[], user_attrs={ - "direction_index": parameters["direction_index"], - "parameters": parameters["abliteration_parameters"], + "parameters": reproduction_information["parameters"], "scores": reproduction_information["scores"], }, ) @@ -910,24 +808,21 @@ def run(): ) print("* Parameters:") - for name, value in get_trial_parameters(trial).items(): + for name, value in modifier.render_trial_parameters(trial).items(): print(f" * {name} = [bold]{value}[/]") # Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893 # once a LoRA is merged it's expected to be empty. Provide a utility function # to restore the previous LoRA-ified state. def reset_trial_model(): + ctx = Context(settings=settings, model=model) print("* Resetting model...") - model.reset_model() - print("* Abliterating...") - model.abliterate( - residual_directions, - trial.user_attrs["direction_index"], - { - k: AbliterationParameters(**v) - for k, v in trial.user_attrs["parameters"].items() - }, + modifier.reset_model(ctx) + print(f"* Modifying model ({modifier_name})...") + parameters = modifier.parameters_class.from_dict( + trial.user_attrs["parameters"] ) + modifier.modify_model(ctx, parameters) reset_trial_model() @@ -1110,12 +1005,11 @@ def run(): # are available on the Hugging Face Hub (not local paths), # that all datasets are pinned to a commit (an unpinned # dataset was likely loaded from a local cache), and that - # only built-in scorer plugins are used (external plugins - # cannot be resolved when reproducing). + # only built-in plugins are used (external plugins cannot + # be resolved when reproducing). dataset_specifications = [ - settings.good_prompts, - settings.bad_prompts, *evaluator.get_dataset_specifications(), + *modifier.get_dataset_specifications(), ] is_reproducible = ( is_hf_path(settings.model) @@ -1126,6 +1020,8 @@ def run(): ) and evaluator.all_scorers_reproducible() and evaluator.all_scorers_builtin() + and modifier.reproducible + and is_builtin_plugin(modifier_entry.config.plugin) and not reproduction_mode ) @@ -1225,6 +1121,7 @@ def run(): card.text = ( get_readme_intro( settings, + modifier, trial, reproducibility_information != "none", ) diff --git a/src/heretic/model.py b/src/heretic/model.py index 9af26b3..2e63032 100644 --- a/src/heretic/model.py +++ b/src/heretic/model.py @@ -1,17 +1,11 @@ # SPDX-License-Identifier: AGPL-3.0-or-later # Copyright (C) 2025-2026 Philipp Emanuel Weidmann + contributors -import math from contextlib import suppress -from dataclasses import dataclass from typing import Any, Type, cast -import bitsandbytes as bnb import torch -import torch.linalg as LA -import torch.nn.functional as F from peft import LoraConfig, PeftModel, get_peft_model -from peft.tuners.lora.layer import Linear from torch import FloatTensor, LongTensor, Tensor from torch.nn import Module, ModuleList from transformers import ( @@ -31,7 +25,7 @@ from transformers.generation import ( GenerateDecoderOnlyOutput, # ty:ignore[possibly-missing-import] ) -from .config import QuantizationMethod, RowNormalization, Settings +from .config import QuantizationMethod, Settings from .system import empty_cache from .utils import Prompt, batchify, format_exception, print @@ -47,14 +41,6 @@ def get_model_class( return AutoModelForCausalLM -@dataclass -class AbliterationParameters: - max_weight: float - max_weight_position: float - min_weight: float - min_weight_distance: float - - class Model: model: PreTrainedModel | PeftModel tokenizer: PreTrainedTokenizerBase @@ -168,11 +154,6 @@ class Model: if self.model is None: raise Exception("Failed to load model with all configured dtypes.") - self._apply_lora() - - # LoRA B matrices are initialized to zero by default in PEFT, - # so we don't need to do anything manually. - print(f"* Transformer model with [bold]{len(self.get_layers())}[/] layers") all_components = {} @@ -186,7 +167,7 @@ class Model: for component, count in all_components.items(): print(f" * [bold]{component}[/]: [bold]{count}[/] modules total") - def _apply_lora(self): + def apply_lora(self, lora_rank: int): # Guard against calling this method at the wrong time. assert isinstance(self.model, PreTrainedModel) @@ -211,13 +192,6 @@ class Model: target_modules = sorted(target_modules_set) - if self.settings.row_normalization != RowNormalization.FULL: - # Rank 1 is sufficient for directional ablation without renormalization. - lora_rank = 1 - else: - # Row magnitude preservation introduces nonlinear effects. - lora_rank = self.settings.full_normalization_lora_rank - self.peft_config = LoraConfig( r=lora_rank, target_modules=target_modules, @@ -233,11 +207,6 @@ class Model: # so the result is a PeftModel rather than a PeftMixedModel. self.model = cast(PeftModel, get_peft_model(self.model, self.peft_config)) - display_targets = sorted({name.rsplit(".", 1)[-1] for name in target_modules}) - print( - f"* LoRA adapters initialized (target types: {', '.join(display_targets)})" - ) - def _get_quantization_config(self, dtype: str) -> BitsAndBytesConfig | None: """ Creates quantization config based on settings. @@ -312,7 +281,7 @@ class Model: self.needs_reload = True return merged_model - def reset_model(self): + def reset_model(self) -> bool: """ Resets the model to a clean state for the next trial or evaluation. @@ -321,6 +290,8 @@ class Model: resets LoRA adapter weights to zero (identity transformation). - Slow path: If switching models or after merge_and_unload(), performs full model reload with quantization config. + + Returns True if the fast path was taken. """ # If a prior model load was interrupted/cancelled mid-process, self.model will be None. @@ -333,7 +304,7 @@ class Model: for name, module in self.model.named_modules(): if "lora_B" in name and hasattr(module, "weight"): torch.nn.init.zeros_(module.weight) - return + return True # Purge existing model object from memory to make space. self.model = None # ty:ignore[invalid-assignment] @@ -360,10 +331,10 @@ class Model: **extra_kwargs, ) - self._apply_lora() - self.needs_reload = False + return False + def get_layers(self) -> ModuleList: model = self.model @@ -458,166 +429,6 @@ class Model: return sorted(components) - def abliterate( - self, - residual_directions: Tensor, - direction_index: float | None, - parameters: dict[str, AbliterationParameters], - ): - if direction_index is None: - residual_direction = None - else: - # The index must be shifted by 1 because the first element - # of residual_directions is the direction for the embeddings. - weight, index = math.modf(direction_index + 1) - residual_direction = F.normalize( - residual_directions[int(index)].lerp( - residual_directions[int(index) + 1], - weight, - ), - p=2, - dim=0, - ) - - # Note that some implementations of abliteration also orthogonalize - # the embedding matrix, but it's unclear if that has any benefits. - for layer_index in range(len(self.get_layers())): - for component, modules in self.get_layer_modules(layer_index).items(): - params = parameters[component] - - # Type inference fails here for some reason. - distance = cast(float, abs(layer_index - params.max_weight_position)) - - # Don't orthogonalize layers that are more than - # min_weight_distance away from max_weight_position. - if distance > params.min_weight_distance: - continue - - # Interpolate linearly between max_weight and min_weight - # over min_weight_distance. - weight = params.max_weight + (distance / params.min_weight_distance) * ( - params.min_weight - params.max_weight - ) - - # A weight of 0 disables this component's ablation. reset_model() has - # already left the adapter at identity, so abort before the otherwise - # wasteful decomposition (which would also be operating on a zero matrix). - if weight == 0: - continue - - if residual_direction is None: - # The index must be shifted by 1 because the first element - # of residual_directions is the direction for the embeddings. - layer_residual_direction = residual_directions[layer_index + 1] - else: - layer_residual_direction = residual_direction - - for module in modules: - # FIXME: This cast is potentially invalid, because the program logic - # does not guarantee that the module is of type Linear, and in fact - # the retrieved modules might not conform to the interface assumed - # below (though they do in practice). However, this is difficult - # to fix cleanly, because get_layer_modules is called twice on - # different model configurations, and PEFT employs different - # module types depending on the chosen quantization. - module = cast(Linear, module) - - # LoRA abliteration: delta W = -lambda * v * (v^T W) - # lora_B = -lambda * v - # lora_A = v^T W - - # Use the FP32 residual direction directly (no downcast/upcast) - # and move to the correct device. - v = layer_residual_direction.to(module.weight.device) - - # Get W (dequantize if necessary). - # - # FIXME: This cast is valid only under the assumption that the original - # module wrapped by the LoRA adapter has a weight attribute. - # See the comment above for why this is currently not guaranteed. - base_weight = cast(Tensor, module.base_layer.weight) - quant_state = getattr(base_weight, "quant_state", None) - - if quant_state is None: - W = base_weight.to(torch.float32) - else: - # 4-bit quantization. - # This cast is always valid. Type inference fails here because the - # bnb.functional module is not found by ty for some reason. - W = cast( - Tensor, - bnb.functional.dequantize_4bit( # ty:ignore[possibly-missing-attribute] - base_weight.data, - quant_state, - ).to(torch.float32), - ) - - # Flatten weight matrix to (out_features, in_features). - W = W.view(W.shape[0], -1) - - if self.settings.row_normalization == RowNormalization.FULL: - # Keep a reference to the original weight matrix so we can subtract it later. - W_org = W - - if self.settings.row_normalization != RowNormalization.NONE: - # Get the row norms. - W_row_norms = LA.vector_norm(W, dim=1, keepdim=True) - # Normalize the weight matrix along the rows. - W = F.normalize(W, p=2, dim=1) - - # Calculate lora_A = v^T W - # v is (d_out,), W is (d_out, d_in) - # v @ W -> (d_in,) - lora_A = (v @ W).view(1, -1) - - # Calculate lora_B = -weight * v - # v is (d_out,) - lora_B = (-weight * v).view(-1, 1) - - if self.settings.row_normalization == RowNormalization.PRE: - # Make the LoRA adapter apply to the original weight matrix. - lora_B = W_row_norms * lora_B - elif self.settings.row_normalization == RowNormalization.FULL: - # Approximates https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration - W = W + lora_B @ lora_A - # Normalize the adjusted weight matrix along the rows. - W = F.normalize(W, p=2, dim=1) - # Restore the original row norms of the weight matrix. - W = W * W_row_norms - # Subtract the original matrix to turn W into a delta. - W = W - W_org - # Use a low-rank SVD to get an approximation of the matrix. - r = self.peft_config.r - - # svd_lowrank is randomized: - # https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109 - # Reseed immediately before the call so restoring a trial is independent of RNG history. - torch.manual_seed(self.settings.seed) - # "It's safe to call this function if CUDA is not available; - # in that case, it is silently ignored." - torch.cuda.manual_seed_all(self.settings.seed) # ty:ignore[invalid-argument-type] - U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6) - - # Truncate it to the part we want to store in the LoRA adapter. - # Note: svd_lowrank actually returns V, so transpose it to get Vh. - U = U[:, :r] - S = S[:r] - Vh = Vh[:, :r].T - # Transfer it into the LoRA adapter components. Split the singular values - # evenly between the two components to keep their norms balanced and avoid - # potential issues with numerical stability. - sqrt_S = torch.sqrt(S) - lora_B = U @ torch.diag(sqrt_S) - lora_A = torch.diag(sqrt_S) @ Vh - - # Assign to adapters. The adapter name is "default", because that's - # what PEFT uses when no name is explicitly specified, as above. - # These casts are therefore valid. - weight_A = cast(Tensor, module.lora_A["default"].weight) - weight_B = cast(Tensor, module.lora_B["default"].weight) - weight_A.data = lora_A.to(weight_A.dtype) - weight_B.data = lora_B.to(weight_B.dtype) - def generate( self, prompts: list[Prompt], @@ -691,6 +502,7 @@ class Model: skip_special_tokens: bool = False, ) -> list[str]: responses = [] + for batch in batchify(prompts, self.settings.batch_size): for response in self.get_responses( batch, @@ -700,7 +512,11 @@ class Model: return responses - def get_residuals(self, prompts: list[Prompt]) -> Tensor: + def get_residuals( + self, + prompts: list[Prompt], + winsorization_quantile: float = 1.0, + ) -> Tensor: # We only generate one token, and we return the residual vectors # at that token position, for each prompt and layer. _, outputs = self.generate( @@ -734,13 +550,13 @@ class Model: # problems during calculations involving residual vectors. residuals = residuals.to(torch.float32) - if 0 <= self.settings.winsorization_quantile < 1: + if 0 <= winsorization_quantile < 1: # Apply symmetric winsorization to each layer of the per-prompt residuals. abs_residuals = torch.abs(residuals) # Get the (prompt, layer, 1) quantiles of the (prompt, layer, component) residuals. thresholds = torch.quantile( abs_residuals, - self.settings.winsorization_quantile, + winsorization_quantile, dim=2, keepdim=True, ) @@ -752,15 +568,28 @@ class Model: return residuals - def get_residuals_batched(self, prompts: list[Prompt]) -> Tensor: + def get_residuals_batched( + self, + prompts: list[Prompt], + winsorization_quantile: float = 1.0, + ) -> Tensor: residuals = [] for batch in batchify(prompts, self.settings.batch_size): - residuals.append(self.get_residuals(batch)) + residuals.append( + self.get_residuals( + batch, + winsorization_quantile=winsorization_quantile, + ) + ) return torch.cat(residuals, dim=0) - def get_residuals_mean(self, prompts: list[Prompt]) -> Tensor: + def get_residuals_mean( + self, + prompts: list[Prompt], + winsorization_quantile: float = 1.0, + ) -> Tensor: if not prompts: raise ValueError("prompts must not be empty") @@ -768,7 +597,10 @@ class Model: total_count = 0 for batch in batchify(prompts, self.settings.batch_size): - batch_residuals = self.get_residuals(batch) + batch_residuals = self.get_residuals( + batch, + winsorization_quantile=winsorization_quantile, + ) # Accumulate in high precision on CPU to reduce peak VRAM usage. batch_sum = batch_residuals.sum(dim=0, dtype=torch.float64).cpu() diff --git a/src/heretic/modifier.py b/src/heretic/modifier.py index a82c276..f17ad97 100644 --- a/src/heretic/modifier.py +++ b/src/heretic/modifier.py @@ -2,16 +2,34 @@ # Copyright (C) 2025-2026 Philipp Emanuel Weidmann + contributors from abc import ABC, abstractmethod -from typing import Generic, TypeVar +from dataclasses import dataclass +from typing import Any, Generic, Protocol, TypeVar, get_args from optuna import Trial +from optuna.trial import FrozenTrial from pydantic import BaseModel -from heretic.plugin import Context, Plugin +from .config import ( + ModifierConfig, +) +from .config import ( + Settings as HereticSettings, +) +from .model import Model +from .plugin import Context, Plugin, load_plugin +from .utils import print -from .config import Settings as HereticSettings -Parameters = TypeVar("Parameters") +class Serializable(Protocol): + def to_dict(self) -> dict[str, Any]: ... + + def to_presentation_dict(self) -> dict[str, str]: ... + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "Serializable": ... + + +Parameters = TypeVar("Parameters", bound=Serializable) class Modifier(Generic[Parameters], Plugin, ABC): @@ -23,6 +41,23 @@ class Modifier(Generic[Parameters], Plugin, ABC): Examples: Standard abliteration, ARA, SOMA, etc. """ + @property + def modifier_name(self) -> str: + """ + The name of the modifier. + This is what shows up in the CLI and Markdown on HF. + """ + return self.__class__.__name__ + + @property + def parameters_class(self) -> type[Parameters]: + """ + The class of the modifier's parameters type. + """ + base_class = self.__class__.__orig_bases__[0] # ty:ignore[unresolved-attribute] + generic_type = get_args(base_class)[0] + return generic_type + def __init__( self, heretic_settings: HereticSettings, @@ -51,3 +86,100 @@ class Modifier(Generic[Parameters], Plugin, ABC): Reset the model (obtainable via `ctx.get_model()`), undoing any changes made by `modify_model`. """ + + def render_trial_parameters(self, trial: Trial | FrozenTrial) -> dict[str, str]: + """ + Transform the names and values of the modifier's parameters + that are contained in the trial's user attributes into a form + suitable for presentation. + """ + return self.parameters_class.from_dict( + trial.user_attrs["parameters"] + ).to_presentation_dict() + + +@dataclass +class ModifierEntry: + modifier: Modifier[Any] + name: str + config: ModifierConfig + + +def load_and_init_modifiers( + settings: HereticSettings, + model: Model, +) -> list[ModifierEntry]: + """ + Load and instantiate all configured modifier plugins, + then runs their initialization hooks. + """ + modifier_configs = settings.modifiers + if not modifier_configs: + raise ValueError("No modifiers configured. Set 'modifiers' in config.toml") + if len(modifier_configs) > 1: + raise ValueError("Using multiple modifiers is not yet supported") + + modifier_keys: set[str] = set() + + modifier_entries: list[ModifierEntry] = [] + + # Resolve plugin classes from names and validate. + for config in modifier_configs: + modifier_cls = load_plugin(name=config.plugin, base_class=Modifier) + modifier_cls.validate_contract() + + print( + f"* Loaded: [bold]{modifier_cls.__name__}{' - ' + config.instance_name if config.instance_name else ''}[/bold]" + ) + + # Instantiate modifiers. + instance_name = config.instance_name or None + + raw_settings = modifier_cls.get_settings_raw( + settings.model_extra, + "modifier", + instance_name, + ) + modifier_settings: BaseModel | None = modifier_cls.validate_settings( + raw_settings + ) + + modifier = modifier_cls( + heretic_settings=settings, + settings=modifier_settings, + ) + + # External labeling key: ensures multiple instances can coexist. + # Uses underscore to match the TOML namespace format (`modifier._`). + modifier_key = ( + modifier_cls.__name__ + if not instance_name + else f"{modifier_cls.__name__}_{instance_name}" + ) + if modifier_key in modifier_keys: + raise ValueError( + f"Duplicate modifier instance name: {modifier_key}. " + "Give each instance a unique `instance_name`." + ) + modifier_keys.add(modifier_key) + + modifier_instance_name = ( + f"{modifier.modifier_name} - {instance_name}" + if instance_name + else modifier.modifier_name + ) + modifier_entries.append( + ModifierEntry( + modifier=modifier, + config=config, + name=modifier_instance_name, + ) + ) + + # Run modifier init hooks. + ctx = Context(settings=settings, model=model) + + for entry in modifier_entries: + entry.modifier.init(ctx) + + return modifier_entries diff --git a/src/heretic/modifiers/__init__.py b/src/heretic/modifiers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/heretic/modifiers/abliteration.py b/src/heretic/modifiers/abliteration.py new file mode 100644 index 0000000..fea5646 --- /dev/null +++ b/src/heretic/modifiers/abliteration.py @@ -0,0 +1,473 @@ +# SPDX-License-Identifier: AGPL-3.0-or-later +# Copyright (C) 2025-2026 Philipp Emanuel Weidmann + contributors + +import math +from dataclasses import asdict, dataclass +from enum import Enum +from typing import Any, cast + +import bitsandbytes as bnb +import torch +import torch.linalg as LA +import torch.nn.functional as F +from optuna import Trial +from peft.tuners.lora.layer import Linear +from pydantic import ( + BaseModel, + Field, + PositiveInt, +) +from torch import Tensor + +from heretic.config import DatasetSpecification +from heretic.modifier import Context, Modifier, Serializable +from heretic.utils import print + + +@dataclass +class WeightDistribution: + max_weight: float + max_weight_position: float + min_weight: float + min_weight_distance: float + + +@dataclass +class Parameters(Serializable): + direction_index: float | None + weight_distributions: dict[str, WeightDistribution] + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + def to_presentation_dict(self) -> dict[str, str]: + parameters = {} + + parameters["direction_index"] = ( + "per layer" + if (self.direction_index is None) + else f"{self.direction_index:.2f}" + ) + + for component, weight_distribution in self.weight_distributions.items(): + for name, value in asdict(weight_distribution).items(): + parameters[f"{component}.{name}"] = f"{value:.2f}" + + return parameters + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "Serializable": + return Parameters( + direction_index=data["direction_index"], + weight_distributions={ + component: WeightDistribution(**weight_distribution) + for component, weight_distribution in data[ + "weight_distributions" + ].items() + }, + ) + + +class RowNormalization(str, Enum): + NONE = "none" + PRE = "pre" + # POST = "post" # Theoretically possible, but provides no advantage. + FULL = "full" + + +class Settings(BaseModel): + good_prompts: DatasetSpecification = Field( + default=DatasetSpecification( + dataset="mlabonne/harmless_alpaca", + split="train[:400]", + column="text", + ), + description="Dataset of prompts that tend to produce desirable responses.", + ) + + bad_prompts: DatasetSpecification = Field( + default=DatasetSpecification( + dataset="mlabonne/harmful_behaviors", + split="train[:400]", + column="text", + ), + description="Dataset of prompts that tend to produce undesirable responses.", + ) + + orthogonalize_direction: bool = Field( + default=True, + description=( + "Whether to adjust the residual directions so that only the component that is " + "orthogonal to the good direction is subtracted during abliteration." + ), + ) + + row_normalization: RowNormalization = Field( + default=RowNormalization.FULL, + description=( + "How to apply row normalization of the weights. Options: " + '"none" (no normalization), ' + '"pre" (compute LoRA adapter relative to row-normalized weights), ' + '"full" (like "pre", but renormalizes to preserve original row magnitudes).' + ), + ) + + full_normalization_lora_rank: PositiveInt = Field( + default=3, + description=( + 'The rank of the LoRA adapter to use when "full" row normalization is used. ' + "Row magnitude preservation is approximate due to non-linear effects, " + "and this determines the rank of that approximation. Higher ranks produce " + "larger output files and may slow down evaluation." + ), + ) + + winsorization_quantile: float = Field( + default=1.0, + description=( + "The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, " + "expressed as the quantile to clamp to (between 0 and 1). Disabled by default. " + 'This can tame so-called "massive activations" that occur in some models. ' + "Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values " + "of the components, then clamps the magnitudes of all components to that quantile." + ), + ) + + +class Abliteration(Modifier[Parameters]): + settings: Settings + + @property + def reproducible(self) -> bool: + return True + + @property + def modifier_name(self) -> str: + if ( + self.settings.orthogonalize_direction + and self.settings.row_normalization == RowNormalization.FULL + ): + return "Magnitude-Preserving Orthogonal Ablation (MPOA)" + elif self.settings.orthogonalize_direction: + return "Projected Abliteration" + else: + return "Abliteration" + + def init(self, ctx: Context) -> None: + model = ctx.get_model() + + print() + print( + f"Loading good prompts from [bold]{self.settings.good_prompts.dataset}[/]..." + ) + self.good_prompts = ctx.load_prompts(self.settings.good_prompts) + print(f"* [bold]{len(self.good_prompts)}[/] prompts loaded") + + print() + print( + f"Loading bad prompts from [bold]{self.settings.bad_prompts.dataset}[/]..." + ) + self.bad_prompts = ctx.load_prompts(self.settings.bad_prompts) + print(f"* [bold]{len(self.bad_prompts)}[/] prompts loaded") + + print() + print("Calculating per-layer residual directions...") + + print("* Obtaining residual mean for good prompts...") + good_means = model.get_residuals_mean( + self.good_prompts, + winsorization_quantile=self.settings.winsorization_quantile, + ) + print("* Obtaining residual mean for bad prompts...") + bad_means = model.get_residuals_mean( + self.bad_prompts, + winsorization_quantile=self.settings.winsorization_quantile, + ) + + self.residual_directions = F.normalize( + bad_means - good_means, + p=2, + dim=1, + ) + + if self.settings.orthogonalize_direction: + # Implements https://huggingface.co/blog/grimjim/projected-abliteration + # Adjust the residual directions so that only the component that is + # orthogonal to the good direction is subtracted during abliteration. + good_directions = F.normalize( + good_means, + p=2, + dim=1, + ) + projection_vector = torch.sum( + self.residual_directions * good_directions, + dim=1, + ) + self.residual_directions = ( + self.residual_directions + - projection_vector.unsqueeze(1) * good_directions + ) + self.residual_directions = F.normalize( + self.residual_directions, + p=2, + dim=1, + ) + + if self.settings.row_normalization != RowNormalization.FULL: + # Rank 1 is sufficient for directional ablation without renormalization. + self.lora_rank = 1 + else: + # Row magnitude preservation introduces nonlinear effects. + self.lora_rank = self.settings.full_normalization_lora_rank + + # LoRA B matrices are initialized to zero by default in PEFT, + # so we don't need to do anything manually. + model.apply_lora(self.lora_rank) + + def suggest_parameters(self, ctx: Context, trial: Trial) -> Parameters: + model = ctx.get_model() + + direction_scope = trial.suggest_categorical( + "direction_scope", + [ + "global", + "per layer", + ], + ) + + last_layer_index = len(model.get_layers()) - 1 + + # Discrimination between "harmful" and "harmless" inputs is usually strongest + # in layers slightly past the midpoint of the layer stack. See the original + # abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis. + # + # Note that we always sample this parameter even though we only need it for + # the "global" direction scope. The reason is that multivariate TPE doesn't + # work with conditional or variable-range parameters. + direction_index = trial.suggest_float( + "direction_index", + 0.4 * last_layer_index, + 0.9 * last_layer_index, + ) + + if direction_scope == "per layer": + direction_index = None + + weight_distributions = {} + + for component in model.get_abliterable_components(): + # The parameter ranges are based on experiments with various models + # and much wider ranges. They are not set in stone and might have to be + # adjusted for future models. + # + # The MLP gets a negative lower bound that is then clamped to 0, so the + # optimizer can fully disable its ablation. The clamp puts a positive + # probability mass on exactly 0 (the continuous sampler would otherwise + # reach 0 with probability zero). Ablating the MLP is often unnecessary for + # removing refusals and tends to damage model intelligence more than + # ablating the attention output, so on many models the optimum is to leave + # it (mostly) untouched. See issue #202. + max_weight_lower_bound = -0.25 if component == "mlp.down_proj" else 0.8 + max_weight = max( + 0.0, + trial.suggest_float( + f"{component}.max_weight", + max_weight_lower_bound, + 1.5, + ), + ) + max_weight_position = trial.suggest_float( + f"{component}.max_weight_position", + 0.6 * last_layer_index, + 1.0 * last_layer_index, + ) + # For sampling purposes, min_weight is expressed as a fraction of max_weight, + # again because multivariate TPE doesn't support variable-range parameters. + # The value is transformed into the actual min_weight value below. + min_weight = trial.suggest_float( + f"{component}.min_weight", + 0.0, + 1.0, + ) + min_weight_distance = trial.suggest_float( + f"{component}.min_weight_distance", + 1.0, + max(0.6 * last_layer_index, 1.0), + ) + + weight_distributions[component] = WeightDistribution( + max_weight=max_weight, + max_weight_position=max_weight_position, + min_weight=(min_weight * max_weight), + min_weight_distance=min_weight_distance, + ) + + return Parameters( + direction_index=direction_index, + weight_distributions=weight_distributions, + ) + + def modify_model(self, ctx: Context, parameters: Parameters) -> None: + model = ctx.get_model() + + if parameters.direction_index is None: + residual_direction = None + else: + # The index must be shifted by 1 because the first element + # of residual_directions is the direction for the embeddings. + weight, index = math.modf(parameters.direction_index + 1) + residual_direction = F.normalize( + self.residual_directions[int(index)].lerp( + self.residual_directions[int(index) + 1], + weight, + ), + p=2, + dim=0, + ) + + # Note that some implementations of abliteration also orthogonalize + # the embedding matrix, but it's unclear if that has any benefits. + for layer_index in range(len(model.get_layers())): + for component, modules in model.get_layer_modules(layer_index).items(): + weight_distribution = parameters.weight_distributions[component] + + # Type inference fails here for some reason. + distance = cast( + float, abs(layer_index - weight_distribution.max_weight_position) + ) + + # Don't orthogonalize layers that are more than + # min_weight_distance away from max_weight_position. + if distance > weight_distribution.min_weight_distance: + continue + + # Interpolate linearly between max_weight and min_weight + # over min_weight_distance. + weight = weight_distribution.max_weight + ( + distance / weight_distribution.min_weight_distance + ) * (weight_distribution.min_weight - weight_distribution.max_weight) + + # A weight of 0 disables this component's ablation. reset_model() has + # already left the adapter at identity, so abort before the otherwise + # wasteful decomposition (which would also be operating on a zero matrix). + if weight == 0: + continue + + if residual_direction is None: + # The index must be shifted by 1 because the first element + # of residual_directions is the direction for the embeddings. + layer_residual_direction = self.residual_directions[layer_index + 1] + else: + layer_residual_direction = residual_direction + + for module in modules: + # FIXME: This cast is potentially invalid, because the program logic + # does not guarantee that the module is of type Linear, and in fact + # the retrieved modules might not conform to the interface assumed + # below (though they do in practice). However, this is difficult + # to fix cleanly, because get_layer_modules is called twice on + # different model configurations, and PEFT employs different + # module types depending on the chosen quantization. + module = cast(Linear, module) + + # LoRA abliteration: delta W = -lambda * v * (v^T W) + # lora_B = -lambda * v + # lora_A = v^T W + + # Use the FP32 residual direction directly (no downcast/upcast) + # and move to the correct device. + v = layer_residual_direction.to(module.weight.device) + + # Get W (dequantize if necessary). + # + # FIXME: This cast is valid only under the assumption that the original + # module wrapped by the LoRA adapter has a weight attribute. + # See the comment above for why this is currently not guaranteed. + base_weight = cast(Tensor, module.base_layer.weight) + quant_state = getattr(base_weight, "quant_state", None) + + if quant_state is None: + W = base_weight.to(torch.float32) + else: + # 4-bit quantization. + # This cast is always valid. Type inference fails here because the + # bnb.functional module is not found by ty for some reason. + W = cast( + Tensor, + bnb.functional.dequantize_4bit( # ty:ignore[possibly-missing-attribute] + base_weight.data, + quant_state, + ).to(torch.float32), + ) + + # Flatten weight matrix to (out_features, in_features). + W = W.view(W.shape[0], -1) + + if self.settings.row_normalization == RowNormalization.FULL: + # Keep a reference to the original weight matrix so we can subtract it later. + W_org = W + + if self.settings.row_normalization != RowNormalization.NONE: + # Get the row norms. + W_row_norms = LA.vector_norm(W, dim=1, keepdim=True) + # Normalize the weight matrix along the rows. + W = F.normalize(W, p=2, dim=1) + + # Calculate lora_A = v^T W + # v is (d_out,), W is (d_out, d_in) + # v @ W -> (d_in,) + lora_A = (v @ W).view(1, -1) + + # Calculate lora_B = -weight * v + # v is (d_out,) + lora_B = (-weight * v).view(-1, 1) + + if self.settings.row_normalization == RowNormalization.PRE: + # Make the LoRA adapter apply to the original weight matrix. + lora_B = W_row_norms * lora_B + elif self.settings.row_normalization == RowNormalization.FULL: + # Approximates https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration + W = W + lora_B @ lora_A + # Normalize the adjusted weight matrix along the rows. + W = F.normalize(W, p=2, dim=1) + # Restore the original row norms of the weight matrix. + W = W * W_row_norms + # Subtract the original matrix to turn W into a delta. + W = W - W_org + # Use a low-rank SVD to get an approximation of the matrix. + r = model.peft_config.r + + # svd_lowrank is randomized: + # https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109 + # Reseed immediately before the call so restoring a trial is independent of RNG history. + torch.manual_seed(self.heretic_settings.seed) + # "It's safe to call this function if CUDA is not available; + # in that case, it is silently ignored." + torch.cuda.manual_seed_all(self.heretic_settings.seed) # ty:ignore[invalid-argument-type] + U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6) + + # Truncate it to the part we want to store in the LoRA adapter. + # Note: svd_lowrank actually returns V, so transpose it to get Vh. + U = U[:, :r] + S = S[:r] + Vh = Vh[:, :r].T + # Transfer it into the LoRA adapter components. Split the singular values + # evenly between the two components to keep their norms balanced and avoid + # potential issues with numerical stability. + sqrt_S = torch.sqrt(S) + lora_B = U @ torch.diag(sqrt_S) + lora_A = torch.diag(sqrt_S) @ Vh + + # Assign to adapters. The adapter name is "default", because that's + # what PEFT uses when no name is explicitly specified, as above. + # These casts are therefore valid. + weight_A = cast(Tensor, module.lora_A["default"].weight) + weight_B = cast(Tensor, module.lora_B["default"].weight) + weight_A.data = lora_A.to(weight_A.dtype) + weight_B.data = lora_B.to(weight_B.dtype) + + def reset_model(self, ctx: Context) -> None: + model = ctx.get_model() + fast_path = model.reset_model() + if not fast_path: + model.apply_lora(self.lora_rank) diff --git a/src/heretic/plugin.py b/src/heretic/plugin.py index 0c62845..f458f13 100644 --- a/src/heretic/plugin.py +++ b/src/heretic/plugin.py @@ -13,17 +13,17 @@ from typing import Annotated, Any, TypeVar, Union, get_args, get_origin, get_typ from pydantic import BaseModel from torch import Tensor -from heretic.utils import Prompt, load_prompts - from .config import DatasetSpecification from .config import Settings as HereticSettings from .model import Model +from .utils import Prompt, deep_merge_dicts, load_prompts T = TypeVar("T") def get_plugin_namespace( - model_extra: dict[str, Any] | None, namespace: str + model_extra: dict[str, Any] | None, + namespace: str, ) -> dict[str, Any]: """ Returns the config dict from the `[]` TOML table. @@ -51,7 +51,7 @@ def is_builtin_plugin(name: str) -> bool: plugins (file paths or third-party import paths) disable the reproducibility offer during upload. """ - return name.startswith("heretic.scorers.") + return name.startswith("heretic.") def load_plugin( @@ -286,9 +286,46 @@ class Plugin: ) return model + @classmethod + def get_settings_raw( + cls, + model_extra: dict[str, Any] | None, + top_namespace: str, + instance_name: str | None, + ) -> dict[str, Any]: + """ + Build the raw settings dict for a plugin class and optional instance. + + Config rules: + - Base settings live in `[.ClassName]` (applies to all instances). + - Instance overrides live in `[.ClassName_]` (preferred). + - Only merge/validate keys that exist in the plugin Settings schema. + """ + settings_model = cls.get_settings_model() + if settings_model is None: + # No settings schema: nothing to merge/validate. + return {} + + class_name = cls.__name__ + + namespaces = [f"{top_namespace}.{class_name}"] + if instance_name: + namespaces.append(f"{top_namespace}.{class_name}_{instance_name}") + + merged_settings: dict[str, Any] = {} + allowed_keys = set(settings_model.model_fields.keys()) + + for namespace in namespaces: + raw_table = get_plugin_namespace(model_extra, namespace) + filtered = {k: v for k, v in raw_table.items() if k in allowed_keys} + merged_settings = deep_merge_dicts(merged_settings, filtered) + + return merged_settings + @classmethod def validate_settings( - cls, raw_namespace: dict[str, Any] | None + cls, + raw_namespace: dict[str, Any] | None, ) -> BaseModel | None: """ Validates plugin settings for this plugin class. @@ -301,6 +338,19 @@ class Plugin: return None return settings_model.model_validate(raw_namespace or {}) + def get_dataset_specifications(self) -> list[DatasetSpecification]: + """ + Collect the dataset specifications declared in the settings + of the plugin. + """ + if self.settings is None: + return [] + specifications = [] + for value in dict(self.settings).values(): + if isinstance(value, DatasetSpecification): + specifications.append(value) + return specifications + def init(self, ctx: Context) -> None: """ Runs before the plugin's main functionality. diff --git a/src/heretic/scorer.py b/src/heretic/scorer.py index b433ec4..5edd0de 100644 --- a/src/heretic/scorer.py +++ b/src/heretic/scorer.py @@ -6,9 +6,8 @@ from dataclasses import dataclass from pydantic import BaseModel -from heretic.plugin import Context, Plugin - from .config import Settings as HereticSettings +from .plugin import Context, Plugin @dataclass diff --git a/src/heretic/scorers/keyword_rate.py b/src/heretic/scorers/keyword_rate.py index 4a936db..b0b3f59 100644 --- a/src/heretic/scorers/keyword_rate.py +++ b/src/heretic/scorers/keyword_rate.py @@ -50,11 +50,6 @@ class Settings(BaseModel): description="Name that describes what the configured keyword rate measures.", ) - keyword_markers: list[str] = Field( - default=REFUSAL_MARKERS, - description="Strings whose presence in a response (case insensitive) identifies the response as a keyword match.", - ) - prompts: DatasetSpecification = Field( default=DatasetSpecification( dataset="mlabonne/harmful_behaviors", @@ -64,6 +59,11 @@ class Settings(BaseModel): description="Dataset of prompts to evaluate the keyword match rate on.", ) + keyword_markers: list[str] = Field( + default=REFUSAL_MARKERS, + description="Strings whose presence in a response (case insensitive) identifies the response as a keyword match.", + ) + print_responses: bool = Field( default=False, description="Whether to print prompt/response pairs when counting keyword matches.", diff --git a/src/heretic/scorers/kl_divergence.py b/src/heretic/scorers/kl_divergence.py index 5cf1747..1739ddb 100644 --- a/src/heretic/scorers/kl_divergence.py +++ b/src/heretic/scorers/kl_divergence.py @@ -17,7 +17,7 @@ class Settings(BaseModel): split="test[:100]", column="text", ), - description="Prompt dataset used to measure KL divergence from original model.", + description="Dataset of prompts used to measure KL divergence from original model.", ) diff --git a/src/heretic/utils.py b/src/heretic/utils.py index 5107394..a8d3595 100644 --- a/src/heretic/utils.py +++ b/src/heretic/utils.py @@ -1,6 +1,8 @@ # SPDX-License-Identifier: AGPL-3.0-or-later # Copyright (C) 2025-2026 Philipp Emanuel Weidmann + contributors +from __future__ import annotations + import hashlib import json import os @@ -11,7 +13,7 @@ from dataclasses import dataclass from datetime import datetime, timezone from importlib.metadata import version from pathlib import Path -from typing import Any, TypeVar +from typing import TYPE_CHECKING, Any, TypeVar import huggingface_hub import tomli_w @@ -38,6 +40,10 @@ from .system import ( is_xpu_available, ) +if TYPE_CHECKING: + from .modifier import Modifier + + T = TypeVar("T") @@ -257,23 +263,9 @@ def batchify(items: list[T], batch_size: int) -> list[list[T]]: return [items[i : i + batch_size] for i in range(0, len(items), batch_size)] -def get_trial_parameters(trial: Trial | FrozenTrial) -> dict[str, str]: - params = {} - - direction_index = trial.user_attrs["direction_index"] - params["direction_index"] = ( - "per layer" if (direction_index is None) else f"{direction_index:.2f}" - ) - - for component, parameters in trial.user_attrs["parameters"].items(): - for name, value in parameters.items(): - params[f"{component}.{name}"] = f"{value:.2f}" - - return params - - def get_readme_intro( settings: Settings, + modifier: Modifier[Any], trial: Trial | FrozenTrial, contains_reproducibility_information: bool, ) -> str: @@ -316,7 +308,7 @@ def get_readme_intro( model_link }, made using [Heretic](https://heretic-project.org) v{version("heretic-llm")} {reproducibility_instructions} -## Abliteration parameters +## {modifier.modifier_name} parameters | Parameter | Value | | :-------- | :---: | @@ -324,7 +316,7 @@ def get_readme_intro( chr(10).join( [ f"| **{name}** | {value} |" - for name, value in get_trial_parameters(trial).items() + for name, value in modifier.render_trial_parameters(trial).items() ] ) } @@ -508,8 +500,7 @@ This directory contains the necessary information and assets to reproduce the re ## Datasets -- **Good prompts:** {format_hf_link(settings.good_prompts.dataset, settings.good_prompts.commit, is_dataset=True)} -- **Bad prompts:** {format_hf_link(settings.bad_prompts.dataset, settings.bad_prompts.commit, is_dataset=True)} +- TODO: Collect all datasets from scorers and modifiers. ## Selected trial @@ -564,8 +555,8 @@ def generate_reproduce_json( version_info = get_heretic_version_info() data = { - # Version 3: plugin-based schema with generic scores/baseline scores. - "version": "3", + # Version 4: plugin-based schema with generic parameters and scores. + "version": "4", "timestamp": timestamp, "system": None, # Defined here to preserve insertion order. "environment": { @@ -578,10 +569,7 @@ def generate_reproduce_json( "requirements": get_requirements_dict(), }, "settings": settings.model_dump(), - "parameters": { - "direction_index": trial.user_attrs["direction_index"], - "abliteration_parameters": trial.user_attrs["parameters"], - }, + "parameters": trial.user_attrs["parameters"], "scores": trial.user_attrs["scores"], "hashes": uploaded_model_hashes, } diff --git a/tests/gemma-4e/config.toml b/tests/gemma-4e/config.toml index d418e7d..5c7d220 100644 --- a/tests/gemma-4e/config.toml +++ b/tests/gemma-4e/config.toml @@ -1,4 +1,4 @@ -# This test case is for Hybrid-Edge models. +# This test case is for hybrid models. # After any change related to it, this test should PASS. model = "tiny-random/gemma-4e" @@ -18,18 +18,6 @@ trial_index = 0 model_action = "save" save_directory = "model" -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" -split = "train[:5]" -column = "text" - -[bad_prompts] -dataset = "mlabonne/harmful_behaviors" -commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" -split = "train[:5]" -column = "text" - [scorer.KLDivergence.prompts] dataset = "mlabonne/harmless_alpaca" commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" @@ -41,3 +29,15 @@ dataset = "mlabonne/harmful_behaviors" commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" split = "test[:5]" column = "text" + +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" +split = "train[:5]" +column = "text" + +[modifier.Abliteration.bad_prompts] +dataset = "mlabonne/harmful_behaviors" +commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" +split = "train[:5]" +column = "text" diff --git a/tests/minicpm5/config.toml b/tests/minicpm5/config.toml index 04093e6..40aa1d6 100644 --- a/tests/minicpm5/config.toml +++ b/tests/minicpm5/config.toml @@ -18,20 +18,6 @@ trial_index = 0 model_action = "save" save_directory = "model" -row_normalization = "none" - -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" -split = "train[:5]" -column = "text" - -[bad_prompts] -dataset = "mlabonne/harmful_behaviors" -commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" -split = "train[:5]" -column = "text" - [scorer.KLDivergence.prompts] dataset = "mlabonne/harmless_alpaca" commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" @@ -43,3 +29,18 @@ dataset = "mlabonne/harmful_behaviors" commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" split = "test[:5]" column = "text" + +[modifier.Abliteration] +row_normalization = "none" + +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" +split = "train[:5]" +column = "text" + +[modifier.Abliteration.bad_prompts] +dataset = "mlabonne/harmful_behaviors" +commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" +split = "train[:5]" +column = "text" diff --git a/tests/mistral-3/config.toml b/tests/mistral-3/config.toml index e04f8e7..3043b9d 100644 --- a/tests/mistral-3/config.toml +++ b/tests/mistral-3/config.toml @@ -1,4 +1,4 @@ -# This test case is for Dense models. +# This test case is for dense models. # After any change related to it, this test should PASS. model = "tiny-random/mistral-3" @@ -18,18 +18,6 @@ trial_index = 0 model_action = "save" save_directory = "model" -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" -split = "train[:5]" -column = "text" - -[bad_prompts] -dataset = "mlabonne/harmful_behaviors" -commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" -split = "train[:5]" -column = "text" - [scorer.KLDivergence.prompts] dataset = "mlabonne/harmless_alpaca" commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" @@ -41,3 +29,15 @@ dataset = "mlabonne/harmful_behaviors" commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" split = "test[:5]" column = "text" + +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" +split = "train[:5]" +column = "text" + +[modifier.Abliteration.bad_prompts] +dataset = "mlabonne/harmful_behaviors" +commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" +split = "train[:5]" +column = "text" diff --git a/tests/qwen2.5/config.toml b/tests/qwen2.5/config.toml index a6923d3..aa0e610 100644 --- a/tests/qwen2.5/config.toml +++ b/tests/qwen2.5/config.toml @@ -18,20 +18,6 @@ trial_index = 0 model_action = "save" save_directory = "model" -row_normalization = "pre" - -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" -split = "train[:5]" -column = "text" - -[bad_prompts] -dataset = "mlabonne/harmful_behaviors" -commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" -split = "train[:5]" -column = "text" - [scorer.KLDivergence.prompts] dataset = "mlabonne/harmless_alpaca" commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" @@ -43,3 +29,18 @@ dataset = "mlabonne/harmful_behaviors" commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" split = "test[:5]" column = "text" + +[modifier.Abliteration] +row_normalization = "pre" + +[modifier.Abliteration.good_prompts] +dataset = "mlabonne/harmless_alpaca" +commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" +split = "train[:5]" +column = "text" + +[modifier.Abliteration.bad_prompts] +dataset = "mlabonne/harmful_behaviors" +commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7" +split = "train[:5]" +column = "text" diff --git a/tests/qwen3.5-moe/config.toml b/tests/qwen3.5-moe/config.toml index 9fbe440..50db428 100644 --- a/tests/qwen3.5-moe/config.toml +++ b/tests/qwen3.5-moe/config.toml @@ -18,18 +18,6 @@ trial_index = 0 model_action = "save" save_directory = "model" -[good_prompts] -dataset = "mlabonne/harmless_alpaca" -commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f" -split = "train[:5]" -column = "text" - 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