def tree_join_eq

bendtorch-core@0.1.0.0 · src/autograd/reduce_replayed_proof.bend:763

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/storage/api.bend as Api
import bendtorch-core@0.1.0.0/src/tensor/parallel.bend as Parallel

763def tree_join_eq(+plan: Shape.BroadcastPlan, +rows: List<&2, Shape.Row>,
764  +rows_exact: {rows == Shape.index_table(Shape.extents(Shape.left_shape(plan)), Shape.extents(Shape.right_shape(plan)), Shape.extents(Shape.output_shape(plan))) : List<&2, Shape.Row>},
765  +shape: Shape.Shape, +fill: F32, +ops: List<&2, StorageLaws.Op>, +h: List<&2, Api.Effect>, +d: Nat, +f: F32, +count: Nat, +lower: Nat, +base: Nat,
766  -a: Array<F32>, high: Array<F32> & (Array<F32> & Unit),
767  ea: {a == out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, base) : Array<F32>},
768  facts: TreeEq(plan, rows, rows_exact, shape, fill, ops, h, d, f, count, lower, Nat.add(Model.capacity(lower), base), high)) ->
769  TreeEq(plan, rows, rows_exact, shape, fill, ops, h, d, f, count, 1n+lower, base, Parallel.join(~(Array<F32> & Unit), a, high)):
770  (b, array) = high
771  (eb, kept) = facts
772  (%Equal.sym(Array<F32>, a, out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, base), ea) : {ANode{_, b} == ANode{out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, base), out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, Nat.add(Model.capacity(lower), base))} : Array<F32>}
773   %Equal.sym(Array<F32>, b, out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, Nat.add(Model.capacity(lower), base)), eb) : {ANode{out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, base), _} == ANode{out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, base), out_spec(plan, Shape.Indexed{rows, rows_exact}, shape, fill, ops, count, lower, Nat.add(Model.capacity(lower), base))} : Array<F32>}
774   {==}, kept)