TorchLean API

NN.Verification.Robustness.TorchLean

TorchLean Robustness Workflow #

End-to-end robustness certification for a TorchLean model.

We build a compact 2-class classifier in TorchLean, compile it to the verifier IR, and certify a margin condition on an ℓ∞ input box using:

Spec we certify (binary logits): ∀ x ∈ [x0-ε, x0+ε], logit0(x) > logit1(x)

Run: lake exe verify -- torchlean-robustness lake exe verify -- torchlean-robustness --float32-mode ieee754exec

Input dimension for the TorchLean robustness example.

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    Hidden width for the TorchLean robustness example.

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      Number of output logits/classes.

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        First-layer weight shape.

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          First-layer bias shape.

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            Second-layer weight shape.

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              Second-layer bias shape.

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                Shape of one input vector supplied to the certified two-layer network.

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                  Output/logit shape.

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                    Parameter shapes list used by the compiled TorchLean program ([W1,b1,W2,b2]).

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                      Compute a (conservative) margin lower bound lo0 - hi1 from logit bounds.

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                        Decide if the output bounds certify logit0 > logit1.

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                          TorchLean program for a 2-layer ReLU MLP producing two logits.

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                            Run the robustness check once under a chosen scalar backend α.

                            This compiles the TorchLean program to the verifier IR, then computes output bounds with IBP and an affine/CROWN-style pass.

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                              CLI entry point for the TorchLean robustness workflow.

                              This is wired into lake exe verify -- torchlean-robustness.

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