def permute_lift

bendtorch-core@0.1.0.0 · src/tensor/permute_values.bend:255

import Base
import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
import bendtorch-core@0.1.0.0/src/tensor/core.bend as Core
import bendtorch-core@0.1.0.0/src/tensor/lifecycle.bend as Lifecycle
import bendtorch-core@0.1.0.0/src/tensor/layout.bend as Layout
import bendtorch-core@0.1.0.0/src/tensor/transforms.bend as Transforms
import bendtorch-core@0.1.0.0/src/tensor/permutation_mapping.bend as Mapping
import bendtorch-core@0.1.0.0/src/tensor/from_values_contract.bend as FromValues
import bendtorch-core@0.1.0.0/src/tensor/contracts/replayed.bend as Replayed

255def permute_lift(+input: Shape.Shape, +values: List<&2, F32>, +axes: List<&2, U32>, +valid: {True{} == Layout.bijective(input, axes) : Bool},
256  -Q: Core.Tensor<input> & Result<&2, &1, Transforms.Error, Core.Dynamic> -> Type,
257  h: @+plan: Layout.Plan<input, axes> -> Final(input, axes, plan, 0.0, FromValues.writes(values, 0n), Shape.logical_numel(Shape.dimensions(Layout.output(input, axes, plan))),
258    pair => Q(Transforms.copy_pack(input, axes, plan, pair))),
259  -source_fits: {Lifecycle.logical_length(Replayed.storage(input, 0.0, FromValues.writes(values, 0n))) == Shape.logical_numel(Shape.dimensions(input)) : Nat}) ->
260  Q(Transforms.permute_copy(input, Core.TTensor{Replayed.storage(input, 0.0, FromValues.writes(values, 0n)), source_fits}, axes)):
261  planned_lift(input, axes, 0.0, FromValues.writes(values, 0n), Layout.permutation(input, axes),
262    Mapping.plan_ranked_success(input, axes,
263      Shape.bool_case(Nat.is_eq(Shape.rank_dimensions(axes), Shape.rank_dimensions(Shape.dimensions(input)))), valid),
264    Q, h, source_fits)