canonical-interp 0.1.1

Efficient tooling for Developmental Interpretability

Canonical Interp: Efficient Developmental Interpretability

Note: This package is under active development (0.X.Y). Breaking changes should be expected between minor versions.

A lean, efficient Local Learning Coefficient (LLC / RLCT) estimator. Rewrite of Timaeus's devinterp. MIT licensed.

What is the LLC?

The Local Learning Coefficient is a measure of effective model complexity at a specific point in weight space, grounded in Singular Learning Theory. For a model trained to a loss minimum $w^{\ast}$, the LLC estimates the real log canonical threshold (RLCT) $\hat\lambda$:

$$\hat\lambda = n\beta \cdot (\bar L_{\text{SGLD}} - L_0)$$

where $L_0$ is the loss at $w^{\ast}$, $\bar L_{\text{SGLD}}$ is the time-averaged loss of SGLD chains run from $w^{\ast}$, and $n\beta$ is an inverse-temperature factor. Higher LLC means the model is using more of its parameter space at that point — lower LLC signals degeneracy or symmetry.

Installation

pip install canonical-interp
# or with uv:
uv add canonical-interp

Requires Python ≥ 3.10 and PyTorch ≥ 2.11.

Quick start

import torch
from torch.utils.data import DataLoader
from canonical_interp.slt import LLCEstimator

n = len(train_dataset)
nbeta = n / math.log(n)  # standard SLT choice

estimator = LLCEstimator(
    draws=200,
    chains=10,
    burnin_steps=100,
    steps_bw_draws=1,
    learning_rate=1e-5,
    localization=100.0,
    nbeta=nbeta,
)

loader = DataLoader(train_dataset, batch_size=512, shuffle=False,
                    pin_memory=True, num_workers=4, persistent_workers=True)

llc = estimator.estimate_llc(model, loader)
print(f"LLC: {llc.mean():.4f}  (per chain: {llc.tolist()})")

criterion_fn defaults to F.cross_entropy. For classification tasks you don't need to pass anything extra.

Examples

Custom loss (regression, custom architectures)

Supply any (logits, targets) -> scalar function. The library handles the functional_call wrapping internally — you don't write it.

import torch.nn.functional as F

llc = estimator.estimate_llc(model, loader, criterion_fn=F.mse_loss)

For more control — e.g. label smoothing, auxiliary losses — pass a lambda or a named function:

def my_loss(logits, targets):
    return F.cross_entropy(logits, targets, label_smoothing=0.1)

llc = estimator.estimate_llc(model, loader, criterion_fn=my_loss)

Custom dataloader format

If your DataLoader yields dicts or tuples with more than two elements, pass an unpack_fn to extract (x, y). Device movement is handled internally.

# DataLoader yields {"input_ids": ..., "labels": ...}
llc = estimator.estimate_llc(
    model, loader,
    unpack_fn=lambda batch: (batch["input_ids"], batch["labels"]),
)

Mixed precision (bfloat16 / float16)

Pass dtype to the constructor. Autocast is applied to the forward pass before vmap and torch.compile see it, so the compiler can fuse dtype casts into the rest of the graph.

estimator = LLCEstimator(
    draws=200, chains=10, burnin_steps=100, steps_bw_draws=1,
    learning_rate=1e-5, localization=100.0, nbeta=nbeta,
    dtype=torch.bfloat16,  # or torch.float16
)

llc = estimator.estimate_llc(model, loader, devices="cuda")

Multi-GPU

Pass a list of devices and set chain_batch to control how many chains run on each device at once. Chain batches are distributed round-robin and run in parallel via a ThreadPoolExecutor.

llc = estimator.estimate_llc(
    model, loader,
    devices=["cuda:0", "cuda:1"],
    chain_batch=5,   # 5 chains per device call; 10 chains total = 2 calls
)

Reproducibility

llc = estimator.estimate_llc(model, loader, seed=42)

A master seed is used to derive independent per-device seeds deterministically, so results are reproducible regardless of how chains are batched across devices.

Performance by default

LLC estimation is compute-intensive: it requires thousands of forward+backward passes across many parallel chains. This library is built around the principle that sensible defaults should leave no performance on the table.

Feature	What it does
vmap over chains	All chains in a batch run as a single fused kernel — no Python loop, no per-chain overhead
torch.compile	The vmapped grad function is JIT-compiled by default (compile=True)
Autocast inside the transform	Autocast wraps the forward pass before vmap/grad see it, so the compiler can fuse dtype casts rather than treating them as opaque boundaries
Expandable CUDA segments	expandable_segments:True is set on CUDA allocators by default, reducing fragmentation from varying tensor sizes across chains and batches. Override with cuda_alloc_conf="backend:cudaMallocAsync" or cuda_alloc_conf=None
DataLoader warnings	The estimator warns when pin_memory=False, num_workers=0, or persistent_workers=False — all of which cause avoidable stalls when the dataloader is cycled for the full SGLD run

Recommended DataLoader settings for GPU runs

loader = DataLoader(
    dataset,
    batch_size=512,
    shuffle=False,       # non-shuffled for deterministic LLC; shuffle for training
    pin_memory=True,     # faster CPU→GPU transfer
    num_workers=4,       # overlap data loading with GPU compute
    persistent_workers=True,  # avoid worker respawn between SGLD epochs
)

Hyperparameter guide

Parameter	Typical range	Effect
nbeta	n / log(n)	Inverse temperature; scales the LLC estimate. Use devinterp.utils.default_nbeta or compute directly.
learning_rate	1e-6 – 1e-4	SGLD step size. Too large → divergence; too small → chain doesn't move.
localization	1 – 1000	Elastic pull toward initial weights. Higher values keep chains near $w^{\ast}$, giving tighter LLC estimates.
burnin_steps	100 – 500	Steps discarded before draws. Should cover transient behaviour.
draws	100 – 500	Samples per chain used to estimate $\bar L$. More draws → lower variance.
chains	5 – 20	Independent chains. More chains → lower variance; all run in parallel via vmap.

How it works

All chain parameters are stacked into a single batched tensor and a single functional forward+backward is vmapped over them, mapping naturally onto GPU parallelism. The statistical procedure follows the SGLD-based LLC estimator from Singular Learning Theory — results can be validated against known closed-form RLCTs (see test_known_rlct.py).

This library was built on top of ideas from devinterp.