def run_eq

bendtorch-core@0.1.0.0 · src/tensor/batched_matmul_PROOF.bend:180

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

180# A run of fuel + 1 terms from lp and rp folds the factor pairs it read and
181# hands both arrays back.
182def run_eq(+fuel: Nat, +dl: Nat, +dr: Nat, -A: Array<F32>, -B: Array<F32>, +ls: Nat, +rs: Nat, +lp: Nat, +rp: Nat, -accumulator: F32,
183  -la: Array<F32>, -ra: Array<F32>, -el: {la == A : Array<F32>}, -er: {ra == B : Array<F32>},
184  -left_valid: Model.Balanced(F32, dl, A), -right_valid: Model.Balanced(F32, dr, B),
185  pl: Array<F32> & F32, -exact_l: {pl == Array.get(F32, la, U32.from_nat(lp)) : Array<F32> & F32},
186  pr: Array<F32> & F32, -exact_r: {pr == Array.get(F32, ra, U32.from_nat(rp)) : Array<F32> & F32}) ->
187  Parallel.Fact<StridedRun<1n+fuel, dl, dr, A, B, ls, rs, lp, rp, accumulator, Matrix.product_run(fuel, ls, rs, lp, rp, accumulator, pl, pr)>>:
188  match fuel:
189    case 0n:
190      run_last(dl, dr, A, B, ls, rs, lp, rp, accumulator, pl, pr,
191        {\{Parallel.Fact: p => p} : Parallel.Fact<Read<dl, dr, A, B, lp, rp, pl, pr>> -> Read<dl, dr, A, B, lp, rp, pl, pr>}(
192          reads(dl, dr, A, B, lp, rp, la, ra, el, er, left_valid, right_valid, pl, exact_l, pr, exact_r)))
193    case 1n+rest:
194      run_next(rest, dl, dr, A, B, ls, rs, lp, rp, accumulator, pl, pr,
195        {\{Parallel.Fact: p => p} : Parallel.Fact<Read<dl, dr, A, B, lp, rp, pl, pr>> -> Read<dl, dr, A, B, lp, rp, pl, pr>}(
196          reads(dl, dr, A, B, lp, rp, la, ra, el, er, left_valid, right_valid, pl, exact_l, pr, exact_r)),
197        value => l => r => el => er => run_eq(rest, dl, dr, A, B, ls, rs, Nat.add(ls, lp), Nat.add(rs, rp), value, l, r, el, er,
198          left_valid, right_valid, Array.get(F32, l, U32.from_nat(Nat.add(ls, lp))), {==}, Array.get(F32, r, U32.from_nat(Nat.add(rs, rp))), {==}))