type LogicalStep

bendtorch-core@0.1.0.0 · src/tensor/batched_matmul_LAWS.bend:216

import Base
import bendtorch-core@0.1.0.0/src/storage/model.bend as Model
import bendtorch-core@0.1.0.0/src/storage/refinement.bend as Refinement
import bendtorch-core@0.1.0.0/src/tensor/matmul.bend as Matrix
import bendtorch-core@0.1.0.0/src/tensor/dot_fold.bend as DotFold

216type LogicalStep<-fuel: Nat, -dl: Nat, -dr: Nat, -A: Array<F32>, -B: Array<F32>, -lo: Matrix.Order, -ro: Matrix.Order, -inner: Nat,
217  -rows: Nat, -columns: Nat, -l1: Nat, -l2: Nat, -r1: Nat, -r2: Nat, -b1: Nat, -b2: Nat, -row: Nat, -column: Nat, -k: Nat,
218  -lv: F32, -rv: F32, -values: List<&2, DotFold.Factors>> is Data:
219  LogicalStep{-left_observed: {Model.observe(F32, dl, A, Refinement.route(dl, U32.from_nat(operand_offset(lo, inner, rows, l1, l2, b1, b2, row, k)))) == Some{lv} : Maybe<&2, F32>},
220    -right_observed: {Model.observe(F32, dr, B, Refinement.route(dr, U32.from_nat(operand_offset(ro, inner, columns, r1, r2, b1, b2, column, k)))) == Some{rv} : Maybe<&2, F32>},
221    -remaining: Logical(fuel, dl, dr, A, B, lo, ro, inner, rows, columns, l1, l2, r1, r2, b1, b2, row, column, 1n+k, values)}