alignment-risk 1.14.0

Template toolkit for AIC-based alignment collapse diagnostics

alignment-risk

alignment-risk is a Python package for pre-flight alignment risk diagnostics during fine-tuning. It estimates whether updates are likely to drift into safety-sensitive directions before you commit to a run.

Full documentation site: sirhan1.github.io/modelFineTuneRiskAssessment

Quick Start (1 minute)

Install:

pip install alignment-risk

Run the built-in demo:

alignment-risk demo --output-dir artifacts

Outputs:

• artifacts/sensitivity_map.png
• artifacts/safety_decay_forecast.png

Installation

PyPI:

pip install alignment-risk

Local development (Apple Silicon convenience):

make setup
source .venv/bin/activate

Local development (manual, cross-platform):

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Basic Usage (Python API)

from alignment_risk import AlignmentRiskPipeline, PipelineConfig

config = PipelineConfig(mode="lora")  # "full" or "lora"
pipeline = AlignmentRiskPipeline(config)

report = pipeline.run(
    model=model,
    safety_dataloader=safety_loader,
    safety_loss_fn=safety_loss_fn,
    fine_tune_dataloader=ft_loader,
    fine_tune_loss_fn=ft_loss_fn,
)

print(report.warning)
print(report.forecast.collapse_step)

CLI

alignment-risk --help
alignment-risk demo --output-dir artifacts
alignment-risk demo --mode lora --output-dir artifacts

What It Computes

1. Low-rank safety sensitivity subspace from empirical Fisher geometry.
2. Initial overlap risk (projection of first update into sensitive subspace).
3. Curvature coupling risk (second-order drift signal).
4. Quartic-style stability forecast and collapse-step estimate.

Modes

• full: analyze all selected trainable parameters.
• lora: analyze only trainable LoRA adapter parameters (lora_, lora_A, lora_B by default).

LoRA Mitigation (AlignGuard-style)

After mode="lora" risk analysis, attach a regularizer to penalize drift in sensitive directions:

from alignment_risk import AlignmentRiskPipeline, PipelineConfig, AlignGuardConfig

config = PipelineConfig(mode="lora")
pipeline = AlignmentRiskPipeline(config)

report = pipeline.run(
    model=model,
    safety_dataloader=safety_loader,
    safety_loss_fn=safety_loss_fn,
    fine_tune_dataloader=ft_loader,
    fine_tune_loss_fn=ft_loss_fn,
)

mitigator = pipeline.build_lora_mitigator(
    model,
    report.subspace,
    config=AlignGuardConfig(lambda_a=0.25, lambda_t=0.5, lambda_nc=0.1, alpha=0.5),
)

task_loss = ft_loss_fn(model, batch)
breakdown = mitigator.regularized_loss(task_loss)
breakdown.total_loss.backward()

Use mitigator.reset_reference() to re-anchor regularization at the current adapter state.

Performance / Accuracy Controls

FisherConfig supports speed/accuracy tradeoffs:

• gradient_collection: "loop" (default), "auto", "vmap".
• subspace_method: "svd", "randomized_svd", "diag_topk".
• vmap_chunk_size: optional chunking for lower memory.
• target_explained_variance: auto-rank selection (default 0.9).

Example:

from alignment_risk import AlignmentRiskPipeline, PipelineConfig

config = PipelineConfig()
config.fisher.gradient_collection = "vmap"
config.fisher.subspace_method = "randomized_svd"
config.fisher.vmap_chunk_size = 16

Theory and Math References

• [AIC-2026] Springer, Max, et al. (2026). The Geometry of Alignment Collapse: When Fine-Tuning Breaks Safety. arXiv:2602.15799v1. PDF: https://arxiv.org/pdf/2602.15799
• [ALIGNGUARD-2025] Das, Amitava, et al. (2025). AlignGuard-LoRA: Alignment-Preserving Fine-Tuning via Fisher-Guided Decomposition and Riemannian-Geodesic Collision Regularization. arXiv:2508.02079v1. PDF: https://arxiv.org/pdf/2508.02079

Detailed internal mappings and equations:

• docs/SOURCES.md
• docs/MATH.md

Development

make install
make test
make lint
make typecheck
make build
make check-dist

See CONTRIBUTING.md for contribution workflow details.