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src/tensor/permute_values_contract_LAWS.bend

bendtorch-core@0.1.0.0 · 68 lines · 4 definitions

1import Base
2import bendtorch-core@0.1.0.0/src/shape/core.bend as Shape
3import bendtorch-core@0.1.0.0/src/tensor/layout.bend as Layout
4import bendtorch-core@0.1.0.0/src/tensor/transpose_spec.bend as Transpose
5import bendtorch-core@0.1.0.0/src/tensor/permute_values_records.bend as Records
6import bendtorch-core@0.1.0.0/src/storage/LAWS.bend as StorageLaws
7import bendtorch-core@0.1.0.0/src/tensor/core.bend as Core
8import bendtorch-core@0.1.0.0/src/tensor/lifecycle.bend as Lifecycle
9import bendtorch-core@0.1.0.0/src/tensor/transforms.bend as Transforms
10import bendtorch-core@0.1.0.0/src/tensor/contracts/replayed.bend as Replayed
11
12# `permute_copy` of a `Core.from_values(input, values)` tensor by a valid
13# permutation succeeds with dimensions `Axis.order(dims, axes)`; output index i
14# holds `values[permute_source(input, axes, i)]`, and the returned source
15# still holds `values[j]` at every j.
16law permute_contract:
17  for +input: Shape.Shape
18  for +values: List<&2, F32>
19  for counted: {List.length(&2, F32, values) == Shape.logical_numel(Shape.dimensions(input)) : Nat}
20  for +axes: List<&2, U32>
21  for valid: {True{} == Layout.bijective(input, axes) : Bool}
22  Records.PermuteContract<input, values, axes>
23
24# `transpose_copy(a, b)` of a `Core.from_values` tensor with both axes in
25# range is the permute contract with the axes that swap a and b.
26law transpose_contract:
27  for +input: Shape.Shape
28  for +values: List<&2, F32>
29  for counted: {List.length(&2, F32, values) == Shape.logical_numel(Shape.dimensions(input)) : Nat}
30  for +a: U32
31  for +b: U32
32  for inside: {True{} == Transpose.valid(input, a, b) : Bool}
33  Records.TransposeContract<input, values, a, b>
34
35# `permute_dynamic` of a replayed tensor, once `Layout.permutation` gives the
36# plan, in the transport form of `broadcast_to_replayed`: for every P, if P
37# holds for every result that hands the source back with the same words and
38# gives an output of the plan's shape, replayed over +0.0, whose cell i holds
39# the source's word at `permute_source(input, axes, i)`, then P holds for the
40# call. The output is a replayed tensor, so a later family law applies to it.
41law permute_replayed:
42  for +input: Shape.Shape
43  for +fill: F32
44  for +ops: List<&2, StorageLaws.Op>
45  for -fits: {Lifecycle.logical_length(Replayed.storage(input, fill, ops)) == Shape.logical_numel(Shape.dimensions(input)) : Nat}
46  for +axes: List<&2, U32>
47  for +plan: Layout.Plan<input, axes>
48  for laid: {Layout.permutation(input, axes) == Done{plan} : Result<&2, &2, Layout.Error, Layout.Plan<input, axes>>}
49  for -P: Core.Dynamic & Result<&2, &1, Transforms.Error, Core.Dynamic> -> Type
50  for h: Records.PermutedWith(input, fill, ops, axes, plan, P)
51  P(Transforms.permute_dynamic(Core.Packed{input, Core.TTensor{Replayed.storage(input, fill, ops), fits}}, axes))
52
53# `transpose_dynamic(a, b)` of a replayed tensor with both axes in range is the
54# permute of `permute_replayed` by the swapped axes, in the same transport form.
55law transpose_replayed:
56  for +input: Shape.Shape
57  for +fill: F32
58  for +ops: List<&2, StorageLaws.Op>
59  for -fits: {Lifecycle.logical_length(Replayed.storage(input, fill, ops)) == Shape.logical_numel(Shape.dimensions(input)) : Nat}
60  for +a: U32
61  for +b: U32
62  for inside: {True{} == Transpose.valid(input, a, b) : Bool}
63  for +plan: Layout.Plan<input, Transpose.swapped_axes(input, a, b)>
64  for laid: {Layout.permutation(input, Transpose.swapped_axes(input, a, b)) == Done{plan} :
65    Result<&2, &2, Layout.Error, Layout.Plan<input, Transpose.swapped_axes(input, a, b)>>}
66  for -P: Core.Dynamic & Result<&2, &1, Transforms.Error, Core.Dynamic> -> Type
67  for h: Records.PermutedWith(input, fill, ops, Transpose.swapped_axes(input, a, b), plan, P)
68  P(Transforms.transpose_dynamic(Core.Packed{input, Core.TTensor{Replayed.storage(input, fill, ops), fits}}, a, b))