def run_eq

bendtorch-core@0.1.0.0 · src/tensor/contracts/matmul_parallel_proof.bend:551

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/matrix_index_proof.bend as MatrixIndex
import bendtorch-core@0.1.0.0/src/tensor/contracts/replayed.bend as Replayed
import bendtorch-core@0.1.0.0/src/storage/api.bend as Api

551# A run of fuel + 1 terms from cursor folds the replayed words in increasing
552# k and hands both copies back.
553def run_eq(+fuel: Nat, +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, +lsrc: Source<left, left_fill, left_ops, ha, dl, fa>, +rsrc: Source<right, right_fill, right_ops, hb, dr, fb>, +rows: Nat, +row: Nat, +column: Nat, +cursor: Nat, +accumulator: F32,
554  +row_below: {Nat.is_lt(row, rows) == True{} : Bool}, +left_extents: {Shape.extents(left) == [rows, span] : List<&2, Nat>},
555  +column_below: {Nat.is_lt(column, columns) == True{} : Bool}, +right_extents: {Shape.extents(right) == [span, columns] : List<&2, Nat>},
556  +steps: {Nat.add(cursor, 1n+fuel) == span : Nat},
557  pl: Array<F32> & F32, +exact_l: {pl == Array.get(F32, Api.arr(ha, dl, fa), Matrix.left_index(span, row, cursor)) : Array<F32> & F32},
558  pr: Array<F32> & F32, +exact_r: {pr == Array.get(F32, Api.arr(hb, dr, fb), Matrix.right_index(columns, column, cursor)) : Array<F32> & F32}) ->
559  DotIs(1n+fuel, span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, row, column, cursor, accumulator, Matrix.product_run(fuel, 1n, columns, Nat.add(Nat.mul(row, span), cursor), Nat.add(Nat.mul(cursor, columns), column), accumulator, pl, pr)):
560  match fuel:
561    case 0n:
562      run_last(span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, lsrc, rsrc, rows, row, column, cursor, accumulator, row_below, left_extents, column_below, right_extents, MatrixIndex.cursor_below(cursor, 0n, span, steps), pl, exact_l, pr, exact_r)
563    case 1n+rest:
564      run_next(rest, span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, lsrc, rsrc, rows, row, column, cursor, accumulator, row_below, left_extents, column_below, right_extents, MatrixIndex.cursor_below(cursor, 1n+rest, span, steps), pl, exact_l, pr, exact_r,
565        run_eq(rest, span, columns, left, left_fill, left_ops, ha, dl, fa, right, right_fill, right_ops, hb, dr, fb, lsrc, rsrc, rows, row, column, 1n+cursor, F32.add(accumulator, F32.mul(Replayed.value(left, left_fill, left_ops, Matrix.left_index(span, row, cursor)), Replayed.value(right, right_fill, right_ops, Matrix.right_index(columns, column, cursor)))), row_below, left_extents, column_below, right_extents, MatrixIndex.steps_next(cursor, 1n+rest, span, steps),
566          Array.get(F32, Api.arr(ha, dl, fa), Matrix.left_index(span, row, 1n+cursor)), {==}, Array.get(F32, Api.arr(hb, dr, fb), Matrix.right_index(columns, column, 1n+cursor)), {==}))