def tree_eq

bendtorch-core@0.1.0.0 · src/tensor/contracts/matmul_parallel_proof.bend:686

import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
import bendtorch-core@0.1.0.0/src/storage/model.bend as Model
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

686def tree_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>,
687  +output: Shape.Shape, +rows: Nat,
688  +left_extents: {Shape.extents(left) == [rows, span] : List<&2, Nat>},
689  +right_extents: {Shape.extents(right) == [span, columns] : List<&2, Nat>},
690  +output_extents: {Shape.extents(output) == [rows, columns] : List<&2, Nat>},
691  +count_exact: {count == Shape.logical_numel(Shape.dimensions(output)) : Nat}, +depth: Nat, +base: Nat) ->
692  TreeEq(span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, count, depth, base, Parallel.tree(~Matrix.Dims, ~Array<F32>, ~Array<F32>, ~Matrix.cell_at, Matrix.dims(Matrix.Dims{span, columns}), depth, count, base, (Api.arr(ha, dl, fa), Api.arr(hb, dr, fb)))):
693  match depth:
694    case 0n:
695      cell_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, Nat.is_lt(base, count), {==})
696    case 1n+lower:
697      match lower:
698        case 0n:
699          pair_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, Matrix.cell_at(Matrix.dims(Matrix.Dims{span, columns}), base, (Api.arr(ha, dl, fa), Api.arr(hb, dr, fb)), Nat.is_lt(base, count)),
700            cell_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, Nat.is_lt(base, count), {==}))
701        case 1n+below:
702          tree_node_eq(span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, count, lower, base, Parallel.tree(~Matrix.Dims, ~Array<F32>, ~Array<F32>, ~Matrix.cell_at, Matrix.dims(Matrix.Dims{span, columns}), lower, count, base, (Api.arr(ha, dl, fa), Api.arr(hb, dr, fb))),
703            tree_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, lower, base),
704            tree_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, lower, Nat.add(Model.capacity(lower), base)))