TorchLean API

NN.Runtime.Autograd.Compiled.IRExec.Correctness.Ops.Loss

Loss #

Loss-function correctness lemmas for the IR → compiled runtime bridge.

The IR node kind .mse_loss is compiled into an SSA node whose forward computes the specification-level mean squared error loss.

This file proves the forward-correctness lemma for that compilation step: on successful compilation at position i, the IR evaluator NN.IR.Graph.denoteAllFrom and the compiled evaluator denoteAllState append the same result.

This structural correctness statement connects the IR semantics to the compiled runtime node. It makes no claim about generalization, training convergence, or the statistical properties of MSE.

Main definitions #

Implementation notes #

References #

Tags #

mse-loss, correctness, ir, runtime, semantic equivalence

theorem Runtime.Autograd.Compiled.buildFrom_denoteAllFrom_mse_loss {α : Type} [Context α] [DecidableEq Spec.Shape] (g : NN.IR.Graph) (payload : NN.IR.Payload α) {inShape : Spec.Shape} {ss : List Spec.Shape} (gd : Proofs.Autograd.Algebra.GraphData α Unit [inShape] ss) (i : ) (st' : IRExec.State α inShape) (x : Spec.Tensor α inShape) (n : NN.IR.Node) (hN : g.getNode i = Except.ok n) (hk : n.kind = NN.IR.OpKind.mseLoss) (hi : i < g.nodes.size) (hBuild : IRExec.buildFrom g payload inShape i ss, gd = Except.ok st') (ih : ∀ (st1 : IRExec.State α inShape), IRExec.buildFrom g payload inShape (i + 1) st1 = Except.ok st'g.denoteAllFrom payload (NN.IR.DVal.mk inShape x) (i + 1) (denoteAllState inShape st1 x) = Except.ok (denoteAllState inShape st' x)) :
g.denoteAllFrom payload (NN.IR.DVal.mk inShape x) i (denoteAllState inShape ss, gd x) = Except.ok (denoteAllState inShape st' x)

Correctness lemma for the .mse_loss node compiler.