def run_eq

bendtorch-core@0.1.0.0 · src/tensor/matmul_parallel_proof.bend:125

import Base
import bendtorch-core@0.1.0.0/src/storage/model.bend as Model
import bendtorch-core@0.1.0.0/src/tensor/matmul.bend as Matrix
import bendtorch-core@0.1.0.0/src/tensor/elementwise_parallel_proof.bend as Parallel

125# A run of fuel + 1 terms from cursor folds the factor pairs it read, and
126# both arrays come back.
127def run_eq(+fuel: Nat, +dl: Nat, +dr: Nat, -A: Array<F32>, -B: Array<F32>, +inner: Nat, +columns: Nat,
128  +row: Nat, +column: Nat, +cursor: Nat, -accumulator: F32,
129  -la: Array<F32>, -ra: Array<F32>, -el: {la == A : Array<F32>}, -er: {ra == B : Array<F32>}, -left_valid: Model.Balanced(F32, dl, A), -right_valid: Model.Balanced(F32, dr, B),
130  pl: Array<F32> & F32, -exact_l: {pl == Array.get(F32, la, Matrix.left_index(inner, row, cursor)) : Array<F32> & F32},
131  pr: Array<F32> & F32, -exact_r: {pr == Array.get(F32, ra, Matrix.right_index(columns, column, cursor)) : Array<F32> & F32}) ->
132  Parallel.Fact<DotRun<1n+fuel, dl, dr, A, B, inner, columns, row, column, cursor, accumulator, Matrix.product_run(fuel, 1n, columns, Nat.add(Nat.mul(row, inner), cursor), Nat.add(Nat.mul(cursor, columns), column), accumulator, pl, pr)>>:
133  match fuel:
134    case 0n:
135      run_last(dl, dr, A, B, inner, columns, row, column, cursor, accumulator, la, ra, el, er, left_valid, right_valid, pl, exact_l, pr, exact_r)
136    case 1n+rest:
137      run_next(rest, dl, dr, A, B, inner, columns, row, column, cursor, accumulator, la, ra, el, er, left_valid, right_valid, pl, exact_l, pr, exact_r,
138        value => l => r => el => er => run_eq(rest, dl, dr, A, B, inner, columns, row, column, 1n+cursor, value, l, r, el, er, left_valid, right_valid,
139          Array.get(F32, l, Matrix.left_index(inner, row, 1n+cursor)), {==}, Array.get(F32, r, Matrix.right_index(columns, column, 1n+cursor)), {==}))