def cell_eq

bendtorch-core@0.1.0.0 · src/tensor/contracts/matmul_parallel_proof.bend:614

import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
import bendtorch-core@0.1.0.0/src/storage/LAWS.bend as StorageLaws
import bendtorch-core@0.1.0.0/src/tensor/matmul.bend as Matrix
import bendtorch-core@0.1.0.0/src/tensor/parallel.bend as Parallel
import bendtorch-core@0.1.0.0/src/storage/api.bend as Api

614def cell_eq(+span: Nat, +columns: Nat, +left: Shape.Shape, +left_fill: F32, +left_ops: List<&2, StorageLaws.Op>, +ha: List<&2, Api.Effect>, +dl: Nat, +fa: F32, +right: Shape.Shape, +right_fill: F32, +right_ops: List<&2, StorageLaws.Op>, +hb: List<&2, Api.Effect>, +dr: Nat, +fb: F32, +count: Nat, +lsrc: Source<left, left_fill, left_ops, ha, dl, fa>, +rsrc: Source<right, right_fill, right_ops, hb, dr, fb>,
615  +output: Shape.Shape, +rows: Nat,
616  +left_extents: {Shape.extents(left) == [rows, span] : List<&2, Nat>},
617  +right_extents: {Shape.extents(right) == [span, columns] : List<&2, Nat>},
618  +output_extents: {Shape.extents(output) == [rows, columns] : List<&2, Nat>},
619  +count_exact: {count == Shape.logical_numel(Shape.dimensions(output)) : Nat}, +base: Nat, inside: Bool,
620  +decided: {inside == Nat.is_lt(base, count) : Bool}) ->
621  TreeEq(span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, count, 0n, base, Parallel.leaf(~(Array<F32> & Array<F32>), Matrix.cell(span, columns, base, Api.arr(ha, dl, fa), Api.arr(hb, dr, fb), inside))):
622  match inside:
623    case False{}:
624      (%decided : {ALeaf{0.0} == ALeaf{out_leaf(span, columns, left, left_fill, left_ops, right, right_fill, right_ops, base, _)} : Array<F32>}
625       {==}, ({==}, {==}))
626    case True{}:
627      cell_inside_eq(span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, count, lsrc, rsrc, output, rows, left_extents, right_extents, output_extents, count_exact, base, decided)