sal-torch

Structurally Adaptive Learning for PyTorch

Training-time sparsification that makes neural networks structurally resilient to compression.

Install

pip install sal-torch            # core
pip install sal-torch[hf]        # + HuggingFace Trainer
pip install sal-torch[reports]   # + PDF/visual reports
pip install sal-torch[crypto]    # + commercial license verification
pip install sal-torch[all]       # everything

from sal import SALConfig, SALCallback

config = SALConfig.auto(model)
trainer = Trainer(model=model, callbacks=[SALCallback(config)])
trainer.train()

Three lines. Any transformer. Compression-resilient.

Fully fine-tune. SAL works by letting the model reorganize around silenced heads, so it needs the weights that do the reorganizing to be trainable. Under LoRA/QLoRA we measured it as actively harmful — see When to use SAL.

CompressionPipeline — the validated path, in one object

The measured recipe is: fully fine-tune with SAL, then compress. Doing that by hand means wiring config → masker → training loop → head selection → slicing → a quantization backend → an eval harness, and getting the training method wrong quietly costs about three points of accuracy. CompressionPipeline makes that path the default and measures every stage, so what comes out is a deployment decision rather than a number.

from sal import CompressionPipeline

pipe = CompressionPipeline(model, eval_dataset, metric="accuracy")

print(pipe.scan().recommendation)      # fragility, absorption map, projected sizes
pipe.sal_train(train_dataset, epochs=3, prune_fraction=0.33)
pipe.compress(pruning=0.33, quantization="int4", slice_heads=True)

report = pipe.validate()
print(report.table)
# measured — GPT-2 Medium / SST-2, one T4 (scripts/modal_v050_test.py)
# stage                    size_mb   ratio    accuracy  seconds
# -------------------------------------------------------------
# original                  1419.3   1.00x      0.5020      0.0
# sal_trained               1419.3   1.00x      0.8965    242.1
# pruned+sliced             1293.4   1.10x      0.8496      0.4
# quantized (int4)           346.5   4.10x      0.8398      3.1

pipe.export("compressed_model/")       # reloaded and checked, not just written
# the exported model reloads at 0.8398 — exactly what was measured
pipe.report().save("compression_report.pdf")

slice_heads=True is what makes the saving real: masking a head makes it behave as if it were gone, slicing removes it from the weight matrices. The exported model runs with no hooks and without sal-torch installed.

Which heads you remove matters more than SAL does. compress() takes a strategy, and the choice is worth making deliberately — measured on GPT-2 Medium, removing the same 120 heads three different ways:

arm           dense    magnitude       random    fi_guided
standard     0.8926       0.8594       0.8867       0.7520
SAL          0.8965       0.8398       0.8906       0.7617

random is the best of the three for both arms and is the default. The obvious heuristic, magnitude, is 2.7 points worse for a standard model and 5.7 for a SAL-trained one — a small weight norm turns out to be a poor proxy for a head the model can spare. fi_guided — spend the budget where the fragility scan says it is cheap — is much worse than either, because concentrating removal does more damage than spreading it.

That grid was one seed, and under random its two arms landed 0.0039 apart — a tie. The five-seed run in What we measured settles it properly: with random selection, SAL leads head pruning by 2.45pp on 5 of 5 seeds. See ROADMAP.md for the full trail.

It refuses LoRA/QLoRA models. Not a warning — an error, with the reason and what to do instead. SAL works by letting the model reorganize around silenced heads, and adapters freeze the weights that would do the reorganizing; measured, SAL under LoRA lost four of six compression variants and gave up 3.1 points of clean accuracy. It also warns below 100M parameters, and an optional accuracy_floor stops the run rather than handing back a model that is small and broken.

The pieces, if you want them separately

from sal import slice_heads, quantize, quantize_info

print(quantize_info(model))   # sizes per method + which backends work here
small = slice_heads(model, heads_to_remove=[(0, 3), (1, 3), ...])
small = quantize(small, method="int4")   # bitsandbytes NF4, or torch.ao INT8

slice_heads requires the same number of heads removed from every layer, and refuses grouped-query attention — architectures store one head count, and removing query heads without whole KV groups corrupts the mapping. It raises in both cases rather than returning something quietly wrong.

Know your model before you touch it

FIScanner — how fragile is this model?

The Fragility Index is a structural diagnostic, scored in [0, 1], that measures how much redundant pathway a model's attention graph has. Heads are compared by their activation signatures; an edge between two heads is fragile when they share no common neighbour, i.e. the function it carries has no backup. FI is the fraction of such edges.

• Low FI → heavily triangulated graph, lots of redundancy → robust.
• High FI → many unsupported edges → fragile under compression.

FI is purely diagnostic — it measures, it never perturbs. You can use it with or without SAL training.

from sal import FIScanner

result = FIScanner(model, probe_dataset).scan()

print(result.fi_score)         # 0.0 - 1.0; lower is more robust
print(result.summary)          # "FI=0.1842 | 3 immune, 2 buffer, 1 critical"
print(result.critical_layers)  # layers whose removal moves FI the most
print(result.immune_layers)    # layers you can compress with little effect

result.save("fragility.json")
result.save("fragility.pdf")   # per-head heatmap (needs sal-torch[reports])

Track it during training with FIMonitor, or call the primitives directly:

from sal import FIMonitor, compute_fi, extract_activation_graph

trainer = Trainer(model=model, callbacks=[FIMonitor(probe_dataset, interval=500)])

adjacency = extract_activation_graph(model, probe_dataset)
fi = compute_fi(adjacency)

PlasticityScanner — where can a model absorb compression?

FI tells you how fragile a model is. PlasticityScanner tells you how much room it has to reorganize, so you know where it is safe to compress. It scores three complementary axes per layer — routing flexibility (attention entropy), inter-layer redundancy (linear CKA), and intra-layer redundancy (an MI proxy) — and folds them into an absorption map that labels each layer ELASTIC (safe), SATURATED (bottleneck), or HUB (compensates when others are pruned).

from sal import PlasticityScanner

pmap = PlasticityScanner(model, probe_dataset).scan()
print(pmap.summary)              # "3 elastic, 1 saturated, 2 hub | mean routing=0.61 ..."

rec = pmap.recommend(target_compression=0.33)
rec.safe_to_prune                # [(layer, head), ...] — prune these first
rec.never_touch                  # heads in hub layers — leave alone
rec.expected_impact              # heuristic accuracy delta

pmap.save("plasticity.json")     # raw scores
pmap.save("plasticity.pdf")      # visual report (needs sal-torch[reports])

Cross-architecture structural profiles

Both scanners are architecture-agnostic, which is only useful if the numbers they return are architecture-specific. Four pretrained checkpoints, no training, one 256-sample probe each (scripts/modal_plasticity_compare.py, ~2-7s per model on a T4):

model	probe	layers	heads	FI	buffer	critical	elastic	saturated	hub
distilbert-base-uncased	text	6	12	0.3086	2	4	1	3	2
gpt2	text	12	12	0.0990	1	11	0	2	10
bert-base-uncased	text	12	12	0.0903	3	9	3	7	2
google/vit-base-patch16-224	vision	12	12	0.0660	2	10	1	5	6

model	routing	CKA	MI
distilbert-base-uncased	0.3141	0.9577	0.0430
gpt2	0.1787	0.9713	0.0610
bert-base-uncased	0.3161	0.9712	0.0363
google/vit-base-patch16-224	0.7110	0.9170	0.0291

• DistilBERT is the fragile one — FI 0.3086, roughly 3× the nearest model (3.1× GPT-2, 3.4× BERT, 4.7× ViT) and the only distilled checkpoint here. Distillation is exactly the process that would strip redundant pathway. It is also the only 6-layer model, so depth is a live confound.
• BERT has more room to compress than GPT-2 — 3 elastic layers against 0, and 2 hub layers against GPT-2's 10. Nearly every GPT-2 layer is doing compensating work, so there is very little the absorption map is willing to call safe.
• GPT-2 and BERT are identical on paper — 12 × 12, 144 heads — and land at opposite ends of that map. Causal and bidirectional attention build different structures at the same size.
• ViT routes far more freely than any text model (0.711 against 0.18–0.32), with the lowest FI and the lowest intra-layer redundancy. GPT-2 is its opposite on every axis. ViT's 6 hub layers sit between BERT's 2 and GPT-2's 10 — a distinct profile, but hub-heaviness is not a vision-specific trait.

The practical point: these four models want four different compression strategies, and the scan that tells you which takes 1.6–6.8s per model on a T4 with no training and no labels. Prune BERT's elastic layers; think much harder before touching GPT-2.

Two honest caveats. No model here has a single IMMUNE layer — the <1% relative-FI threshold looks unreachable on real pretrained checkpoints, so treat IMMUNE as theoretical at the current calibration. And the ViT row is probed with images while the other three get sentences: the three text models are a controlled comparison, ViT is a fourth architecture on its own probe.

FI ranks structure; it does not predict compression survival. ViT-base has the lowest FI in this table and still gives up 27 accuracy points at 33% head pruning (see What we measured). Use RobustnessTest for survival questions and FI for structural ones.

Load with attn_implementation="eager", or the model returns no attention weights, routing entropy comes back NaN, and hub detection quietly reports zero hubs — which reads exactly like a finding.

Self-supervised encoders scan too. I-JEPA ViT-H/14 (facebook/ijepa_vith14_1k, 631M, 32 × 16) loads through AutoModel and needs no special handling — the architecture registry does not list ijepa, but the scanners locate its attention projections anyway. Against ViT-base and a ViT-large depth control on the same probe, most of its profile is a depth trend rather than an objective effect: its FI (0.0040) is indistinguishable from ViT-large's (0.0041). Two axes are not. Its heads are about twice as correlated within a layer as either supervised ViT, and its routing entropy falls with depth where both supervised models rise — broad early, narrow late. See scripts/modal_jepa_scan.py and scripts/jepa_scan_results.json.

sal.compare() — SAL vs. other pruning methods

Benchmark SAL against post-hoc baselines at a matched compression level and see which keeps the most accuracy (or lowest loss) after heads are removed.

from sal import compare

result = compare(model, train_dataset, eval_dataset,
                 methods=["sal", "magnitude", "random_posthoc"],
                 compression=0.33, sal_epochs=3, metric="accuracy")
print(result.table)              # method | score | pruned_heads | time
print(result.winner)
result.save("comparison.pdf")    # bar chart + table

# plug in your own method
compare.register_method("my_pruner", lambda model, ds, eval_ds, ctx: my_score)

Does it survive real compression?

We polled practitioners on how they actually compress models. Of 33 responses, 39% quantize (INT8/INT4) — more than pruning and distillation. SAL was built against head pruning, so the honest question is whether the resilience it trains in generalizes to the compression people actually ship.

Short answer: yes, if you fully fine-tune — and the win is a pruning win before it is a quantization win. On the primary evidence, five seeds of GPT-2 Medium, a SAL-trained model keeps 2.45pp more accuracy under 33% head pruning on 5 of 5 seeds at no cost to clean accuracy; INT4 alone is a coin flip, and under LoRA the whole thing loses. A preliminary three-seed vision study points the same way and much larger — cross-modal validation is under investigation, not established. The numbers, including the rows SAL did not win, are in What we measured.

RobustnessTest — one model, every degradation

from sal import RobustnessTest

test = RobustnessTest(model, eval_dataset, metric="accuracy")
report = test.run(methods=["int8", "int4", "head_pruning_33", "head_pruning_50",
                           "neuron_dropout_10", "neuron_dropout_20"])

print(report.table)
# real output — DistilBERT fine-tuned on SST-2, no SAL, 512 eval examples
# method              baseline     after     delta     std  survived
# ------------------------------------------------------------------
# int8                  0.8594    0.8359   -0.0234       -        OK
# int4                  0.8594    0.8672   +0.0078       -        OK
# head_pruning_33       0.8594    0.7656   -0.0938       -      FAIL
# head_pruning_50       0.8594    0.7363   -0.1230       -      FAIL
# neuron_dropout_10     0.8594    0.8392   -0.0202   0.015        OK
# neuron_dropout_20     0.8594    0.8320   -0.0273   0.022        OK

print(report.robustness_score)   # aggregate 0-1: mean quality retained
print(report.survival_rate)      # fraction of methods survived

report.save("robustness.json")
report.save("robustness.pdf")    # bars + retention radar (needs sal-torch[reports])

A method counts as survived when relative degradation stays within survival_threshold (default 5% of the clean baseline). Methods:

Method	What it does
int8	Dynamic INT8 over every nn.Linear (torch.ao.quantization, CPU)
int4	4-bit weight-only — bitsandbytes NF4 when available, otherwise a simulated per-channel INT4 round-trip (the backend used is recorded on each result)
head_pruning_<pct>	Silences <pct>% of attention heads using the shipped HeadMasker
neuron_dropout_<pct>	Zeroes <pct>% of FFN neurons at inference — dead units / noisy hardware. Repeated over several fault patterns; mean ± std reported

pip install sal-torch[quant] adds bitsandbytes for real NF4. Without it, int4 falls back to simulation rather than disappearing from your report — pass allow_simulated_quant=False if you would rather see the row skipped.

robustness_compare() — SAL-trained vs. standard

from sal import robustness_compare

result = robustness_compare(
    sal_model=sal_trained_model,
    baseline_model=standard_model,
    eval_dataset=eval_dataset,
    methods=["int8", "int4", "head_pruning_33"],
    metric="accuracy",
)

print(result.table)
print(result.summary)
result.save("robustness_comparison.pdf")

Each model is scored against its own clean baseline, so the comparison measures resilience rather than which model was better to begin with. The row winner is whichever model loses proportionally less.

What we measured

The primary evidence is GPT-2 Medium at five seeds. A three-seed vision study follows it as secondary, preliminary evidence — promising, and not yet enough to call SAL cross-modally validated.

Five seeds, full eval split — GPT-2 Medium (primary evidence)

Every number this project published before v0.5.0 was a single seed, which is not enough: two GPT-2 baselines trained on identical data differ by about a point, and one single-seed conclusion here has already been overturned. So the v0.4.0 protocol was re-run five times.

GPT-2 Medium / SST-2, full fine-tuning, seeds 42/123/456/789/1337, scored on the complete 872-example validation split. Each seed trains the same checkpoint twice — plain, and with SAL at prune_fraction=0.33 — then puts both through the same battery, with both arms losing the same heads.

variant	standard	SAL	delta	SAL ahead on	consistent?
dense	0.9005 ± 0.0104	0.9062 ± 0.0080	+0.57pp	4/5	yes
int8	0.8888 ± 0.0102	0.8961 ± 0.0033	+0.73pp	4/5	yes
int4	0.9002 ± 0.0111	0.9023 ± 0.0094	+0.21pp	3/5	no
prune33	0.8571 ± 0.0240	0.8817 ± 0.0121	+2.45pp	5/5	yes
prune50	0.8087 ± 0.0455	0.8294 ± 0.0368	+2.06pp	4/5	yes
prune33+int8	0.8284 ± 0.0090	0.8472 ± 0.0229	+1.88pp	4/5	yes
prune33+int4	0.8567 ± 0.0208	0.8725 ± 0.0132	+1.58pp	5/5	yes

Mean ± sample std across seeds. "Consistent" means SAL led on 4 or more of the 5 seeds — a sign test on the direction, which is the question a single seed cannot answer, not a t-test on the magnitude.

What holds. SAL wins 5 of 6 compressed variants, and it costs nothing on the clean model (+0.57pp — SAL is not buying resilience with accuracy). The effect is largest and most reliable exactly where SAL was designed to work: head pruning, +2.45pp at 33% on every seed and +2.06pp at 50%. Both combined recipes hold too.

What does not. int4 on its own does not replicate. Three seeds of five and +0.21pp — smaller than the run-to-run spread, i.e. a coin flip. The v0.4.0 claim that SAL won all seven variants including INT4 was one seed; across five, quantization-only gains are small, and the INT4 row is the weakest in the table. Where SAL helps under INT4 is in combination with pruning (prune33+int4, +1.58pp on 5/5) — which is the pruning effect carrying the row.

About the Pareto claim. v0.4.0 reported SAL/int4 beating the uncompressed standard model. Across five seeds it averages 0.9023 at 361.9MB against the standard model's 0.9005 at 1419.3MB. That +0.18pp is inside the spread, so the honest statement is equal accuracy at a quarter of the size, not higher. It is still the deployment recommendation; it is not a free accuracy gain.

One observation, offered as such. The SAL arm has the smaller standard deviation on 6 of the 7 variants — most sharply on prune33 (0.0121 vs 0.0240). Five seeds is too few to call this a property, but SAL-trained models were more predictable under compression here, not merely better on average.

Reproduce with scripts/modal_multiseed_validation.py (5× Modal T4, ~10 min); raw per-seed numbers in scripts/multiseed_results.json.

One caveat on the int8 rows. This run quantizes INT8 with torch.ao dynamic quantization (CPU), where v0.4.0 used bitsandbytes LLM.int8() on CUDA. The INT8 rows are therefore a different measurement, not a replication of the earlier ones. INT4 is bitsandbytes NF4 in both.

Vision transformers — secondary, preliminary

Cross-modal validation is under investigation, not established. What follows is three seeds on one architecture and one task. It is the beginning of an answer, not the answer.

ViT-base-patch16-224 on CIFAR-10, full fine-tuning, seeds 42/123/456, 1000 eval images, same battery structure as above (scripts/modal_vit_validation.py):

variant	standard	SAL	delta	SAL ahead on
dense	0.9673 ± 0.0029	0.9570 ± 0.0075	−1.03pp	0/3
int8	0.9603 ± 0.0042	0.9550 ± 0.0056	−0.53pp	1/3
prune33	0.7013 ± 0.0472	0.8450 ± 0.0070	+14.37pp	3/3
prune50	0.3143 ± 0.0550	0.4760 ± 0.1120	+16.17pp	3/3
prune33+int8	0.5723 ± 0.0549	0.7727 ± 0.0328	+20.03pp	3/3

The pruning gains are large and unanimous. Read them with four things attached:

• SAL costs clean accuracy here. −1.03pp on dense, losing all three seeds, where on GPT-2 it cost nothing. Two architectures, two answers.
• Quantization-only does nothing, as on GPT-2. INT8 barely dents ViT at all (96.7% → 96.0%), so there was no damage to recover.
• The two studies are not on a common scale. CIFAR-10 is 10-way, SST-2 is 2-way, so raw percentage points do not transfer. Normalized to headroom above chance, SAL moves prune33 retention from 69.3% to 86.9% here, against +4.7pp on GPT-2. The gap narrows under the correction but does not close.
• Three seeds is thin, and "ahead on 3/3" is unanimity, not significance.

What we do not yet know is why the gap is this large. The consistent pattern across every run since v0.4.0 is that SAL recovers damage in proportion to how much damage there is — ViT-base collapses under head pruning (96.7% → 70.1% at 33%) where GPT-2 Medium degrades gently, so there is far more to recover. That reading fits, but it is a post-hoc fit to two data points. Note that it is not a model-size story: the +20pp came from the smaller model (ViT-base, 86M) and the +2.45pp from the larger one (GPT-2 Medium, 355M). Size, modality, architecture and task-difficulty are all confounded across these two studies, and nothing here separates them. Do not assume your model lands at either end — measure it with RobustnessTest.

The earlier single-seed runs

The four runs below are what motivated the multi-seed study. They stay here because the two SAL lost are what established the LoRA finding. Each trains one model twice from identical weights, then evaluates both dense under the battery. Scripts in scripts/; results in scripts/robustness_scale_results.json. Single seed each — read them as signals, not benchmarks.

run	model / task	training	clean cost	quantization	pruning	combined
v0.4.0	DistilBERT / SST-2	full FT	+1.17pp	1/2	2/2	not tested
scale	GPT-2 Medium / SST-2	LoRA r=16	-3.12pp	0/2	1/2	1/2
scale	GPT-2 Medium / SST-2	full FT	+0.39pp	2/2	2/2	2/2
scale	Phi-2 2.7B / MMLU	LoRA r=16	-1.17pp	0/2	1/2	0/2

The DistilBERT run also tested inference-time neuron dropout at 10% and 20%, which the standard model won both times — by 0.5pp and 0.6pp, inside that run's noise floor. The scale runs do not test dropout, so it has no column here.

The two GPT-2 rows are a controlled comparison: identical model, task, data, seed and battery. The only thing that changes is whether LoRA is in the way.

Under LoRA, the same setup fails

variant           baseline       SAL     delta    winner
--------------------------------------------------------
dense               0.8906    0.8594   -0.0312  baseline
int8                0.8828    0.8613   -0.0215  baseline
int4                0.8594    0.8301   -0.0293  baseline
prune33             0.8496    0.8418   -0.0078  baseline
prune50             0.8145    0.8496   +0.0352       SAL
prune33+int8        0.8496    0.8477   -0.0020  baseline
prune33+int4        0.8301    0.8359   +0.0059       SAL

Same model, same data, same seed. SAL loses four of six compressed variants and gives up 3.1 points of clean accuracy to get there. Only the heaviest structural damage (prune50) still favours it.

We are leaving this table in the README because it is the finding that explains the mechanism: SAL works by letting the model reorganize around silenced heads, and LoRA freezes the weights that would do the reorganizing. Rank-16 adapters on c_attn cannot absorb 126 silenced heads. The perturbation lands, the adaptation cannot.

What is not established

• Quantization-only resilience. Five seeds put int4 at +0.21pp on 3/5 — no consistent effect. int8 is consistent but small (+0.73pp), and measured on a different backend than v0.4.0's. If you never prune, do not assume SAL buys you anything at INT4; measure it on your model.
• Scale is still open. Phi-2 2.7B was LoRA-only, so "LoRA starves it" and "SAL stops working above ~350M" remain confounded at that size. Phi-2 under full fine-tuning is the experiment that separates them.
• Cross-modal validation. Under investigation. One vision architecture on one task at three seeds is a preliminary result, not a validated claim, and the README should not be read as making one.
• Why the effect size differs so much between the two studies. ViT gained +14 to +20pp under pruning where GPT-2 Medium gained +2.45pp. The pattern fits "SAL recovers damage in proportion to how much there is", but that is a post-hoc reading of two points. It is not a size story — the larger gain came from the smaller model — and size, modality, architecture and task difficulty are confounded across the two runs.
• Whether SAL costs clean accuracy. It cost nothing on GPT-2 (+0.57pp on 5 seeds) and 1.03pp on ViT (0/3 seeds). Two architectures, two answers.
• Everything else is single-seed. DistilBERT and Phi-2 have one run each, and nothing above 355M has been fully fine-tuned at all.
• Everything under LoRA. The negative LoRA result is itself single-seed. It is consistent with the mechanism, and it agrees across two model sizes, but it has not had the same treatment.

When to use SAL

your setup	recommendation
Full fine-tuning, and you prune heads	Yes. The strongest and best-replicated case: +2.45pp at 33% pruning on 5/5 seeds of GPT-2 Medium, at no cost to clean accuracy.
Vision transformers	Promising, preliminary. Three seeds on ViT-base/CIFAR-10 gave +14 to +20pp under pruning, unanimously — but it cost 1.03pp of clean accuracy, and one architecture on one task is not cross-modal validation. Measure yours.
Full fine-tuning, quantization only	Measure first. INT8 gave +0.73pp on GPT-2 (4/5 seeds) and nothing on ViT; INT4 alone was a coin flip (3/5, +0.21pp). Use RobustnessTest on your own model before committing.
Self-supervised vision encoders	Yes, at 33–50% head pruning, with modest expectations. I-JEPA ViT-H/14: +0.4 to +1.9pp, 35/36 wins over 3 seeds. No effect at 70%. DINOv2 is single-seed.
LoRA / QLoRA adapters	Not recommended. Measured worse than not using SAL at all, and it costs clean accuracy. The adapters are too small to redistribute what the masking removes.
Models above ~1B	Unvalidated. No full-fine-tuning result at that scale yet.

If you are on LoRA and want compression resilience, the honest answer today is that SAL is not the tool; use RobustnessTest to measure what your compression actually costs and PlasticityScanner to choose where to cut.

Continual learning without replay buffers

StructuralGuard — protect what matters when you fine-tune

When you fine-tune a trained model on a new task, it quietly overwrites the structure that carried the old one. StructuralGuard reads the model's structural map and freezes the critical attention heads (hub layers, structural bottlenecks, and the functionally unique heads) while leaving the redundant heads free to absorb the new task. No EWC, no replay buffer, no distillation — the topology itself decides what to protect.

from sal import StructuralGuard

# After training on task A, build a guard from the model's structure.
guard = StructuralGuard.from_model(model, probe_dataset, protection_level=0.5)

print(guard.protected_heads)   # [(layer, head), ...] frozen during fine-tuning
print(guard.trainable_heads)   # [(layer, head), ...] free to absorb task B
print(guard.protection_map)    # {layer: [protected head indices]}

guard.protect(model)           # zero gradients for protected heads (backward hooks)
trainer.train()                # fine-tune on task B with ANY training loop
guard.release()

drift = guard.measure_drift(model, probe_dataset=probe_dataset)
print(drift.forgetting_score)      # 0 = nothing forgot, 1 = total reorganization
print(drift.protected_integrity)   # ~1.0 if the protected heads held

guard.save("model_guard.json")     # serialize; reload before task C, D, ...
guard = StructuralGuard.load("model_guard.json")

Protection is at the head level — some heads in a layer can be frozen while others in the same layer keep learning. protection_level (0.0–1.0) sets the fraction of the most critical heads to protect.

HuggingFace Trainer? Use the callback — it applies protection on train_begin, measures drift on train_end:

from sal import StructuralGuardCallback

guard = StructuralGuard.from_model(model, probe_dataset)
callback = StructuralGuardCallback(guard)
trainer = Trainer(model=model, callbacks=[callback])
trainer.train()
print(callback.drift_report.summary)

DriftMonitor — measure structural forgetting after any fine-tuning

DriftMonitor quantifies how much a model's structure moved, guarded or not. Snapshot before and after, then compare.

from sal import DriftMonitor

monitor = DriftMonitor(model, probe_dataset)
monitor.snapshot("before_task_b")
trainer.train()
monitor.snapshot("after_task_b")

drift = monitor.compare("before_task_b", "after_task_b")
print(drift.summary)
print(drift.layer_drift)             # per-layer activation retention (1 = identical)
print(drift.classification_changes)  # layers whose fragility class flipped
drift.save("drift_report.json")
drift.save("drift_report.pdf")       # visual before/after comparison

Snapshots are keyed, so you can track drift across many sequential tasks and compare any pair.

Self-supervised models (v0.5.1)

SALTrainer no longer assumes your loss is cross-entropy. Pass train_step= and you own the step; SAL keeps owning the prune schedule.

def my_train_step(model, batch, optimizer, mask_module):
    loss = my_custom_loss(model, batch)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    return loss.item()

trainer = SALTrainer(model, config, optimizer, dataloader,
                     train_step=my_train_step)
trainer.train(num_epochs=5)

The masker is already installed and already masking when your callback is entered, so a callback that ignores mask_module entirely is still SAL-trained. You get it as the fourth argument for the case that needs it — reading an unperturbed target mid-step:

with torch.no_grad(), mask_module.unmasked():
    target = model(pixel_values=pixels).last_hidden_state   # no heads masked
predicted = model(pixel_values=visible).last_hidden_state   # heads masked

Use unmasked() (or remove_mask() / apply_mask()) rather than deactivate(). The first pair suspends masking; deactivate() resets the pruned set to empty and silently undoes the schedule's accumulated damage.

train_step=None keeps the v0.5.0 cross-entropy loop exactly as it was.

Scoring a model that has no accuracy

A self-supervised encoder has no head, so "did compression hurt?" has to be asked about the representations. sal.evaluation:

from sal import linear_probe, knn_accuracy, cka_similarity, measure_latency

probe = linear_probe(model, train_loader, val_loader)     # frozen encoder + linear head
knn   = knn_accuracy(model, train_loader, val_loader, k=20)
cka   = cka_similarity(original, compressed, loader)      # 1.0 = identical, no labels needed
ms    = measure_latency(compressed, device="cuda")        # median, not mean

cka_similarity is the one to reach for first: it is label-free and invariant to an invertible linear map, so a model whose heads have been physically sliced out is still comparable to the original despite the width change.

compression_report() bundles all of it, and reports any metric it could not compute as None with the reason under "skipped" — an unexplained None in a benchmark table is indistinguishable from a measured zero.

Seeing what changed

from sal.visualization import compare_feature_maps
compare_feature_maps(original, compressed, image, save_path="before_after.png")

Both rows share one colour scale, deliberately. Per-panel normalization rescales a model whose activations collapsed toward zero back up to full range, so it renders as identical to the original — hiding the exact failure the figure exists to show.

Architectures: ijepa and dinov2 join the auto-detected list.

Self-supervised vision results (v0.5.1)

ImageNet-100, five epochs per arm on one A100. The SAL arm trains with mask_ratio=0.3; the control trains identically without head masking. Both are then pruned post hoc and scored on frozen representations.

I-JEPA ViT-H/14 (631M) — SAL wins 35 of 36 paired comparisons across three seeds:

pruning	metric	SAL	control	delta	SAL ahead
33% random	linear probe	76.8%	76.4%	+0.4pp	3/3
33% random	kNN	71.2%	70.0%	+1.2pp	3/3
50% random	linear probe	64.3%	62.9%	+1.4pp	3/3
50% random	kNN	47.5%	45.6%	+1.9pp	3/3

Consistent, and small: read these as "SAL does not hurt and reliably helps a little", not as the +2.45pp GPT-2 or +14pp ViT-base effect. No cost on the unpruned model (83.2% vs 83.1% probe). At 70% pruning SAL and control are indistinguishable, whether SAL trains at 30% or 70% masking.

DINOv2 ViT-L/14 (304M) — one seed, SAL wins 13 of 18. DINOv2 is much more fragile to head removal: at 50% both arms collapse. Where it survives, the gap is larger:

pruning	metric	SAL	control	delta
33% sliced (random per layer)	linear probe	84.0%	81.0%	+3.0pp
33% sliced (random per layer)	kNN	75.4%	68.5%	+6.9pp

What slicing buys. slice_heads() on I-JEPA at 50% of heads: 631M → 526M parameters, 1.10× GPU and 1.20× CPU latency at batch 1. Head removal only narrows attention, which is a third of a ViT block, so expect ~1.1–1.3× from heads alone, not 2×.

Caveats: the training objective is I-JEPA-shaped masked prediction, applied to DINOv2 as well (not its self-distillation objective); DINOv2, slicing and 70% are single-seed. Raw numbers in data/results/.

See examples/jepa_sal.py, scripts/run_jepa_sal.py (standalone — any GPU box over SSH, bash scripts/setup_runpod.sh to prepare one) and scripts/modal_jepa_sal.py (Modal). Read the caveats in all of them — in particular, Meta never released an I-JEPA ViT-B/16, the training objective is I-JEPA-shaped rather than I-JEPA, and the default table compares head selection rather than SAL training (--control adds the arm that tests SAL training itself).

Examples

• examples/quickstart.py — 3-line SAL training on DistilBERT
• examples/standalone_fi.py — Fragility Index scan, no training
• examples/full_control.py — manual config + standalone trainer
• examples/compare_with_without_sal.py — SAL vs. baseline under compression
• examples/jepa_sal.py — SAL on a self-supervised vision encoder, with a custom training step (--smoke runs on CPU)

New here? Start with docs/getting_started.md.

Roadmap

See ROADMAP.md for what's shipped, what's next, and how to request features — including the full evidence trail behind the robustness claims, losses included. v0.5.0 shipped CompressionPipeline, slice_heads(), quantize(), and the five-seed validation above. v0.5.1 opened SAL to self-supervised encoders. Next up is SAL + quantization-aware training and MLP pruning (v0.6.0), aiming for a real 2× speedup.

License

BSL 1.1 — free for research and evaluation. Commercial production requires a license.

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