Mercurial > repos > public > sbplib_julia

comparison src/LazyTensors/lazy_tensor_operations.jl @ 1037:9e76bf19904c refactor/lazy_tensors

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Drop more prefixes
author Jonatan Werpers <jonatan@werpers.com>
date Tue, 22 Mar 2022 14:10:53 +0100
parents 6abbb2c6c3e4
children f857057e61e6 3bb94ce74697 2e606d4c0ab1
comparison
equal deleted inserted replaced
1017:6abbb2c6c3e4 1037:9e76bf19904c
26 Base.@propagate_inbounds Base.getindex(ta::TensorApplication{T,1} where T, I::CartesianIndex{1}) = ta[Tuple(I)...] # Would otherwise be caught in the previous method. 26 Base.@propagate_inbounds Base.getindex(ta::TensorApplication{T,1} where T, I::CartesianIndex{1}) = ta[Tuple(I)...] # Would otherwise be caught in the previous method.
27 Base.size(ta::TensorApplication) = range_size(ta.t) 27 Base.size(ta::TensorApplication) = range_size(ta.t)
28 28
29 29
30 """ 30 """
31 LazyTensorTranspose{T,R,D} <: LazyTensor{T,D,R} 31 TensorTranspose{T,R,D} <: LazyTensor{T,D,R}
32 32
33 Struct for lazy transpose of a LazyTensor. 33 Struct for lazy transpose of a LazyTensor.
34 34
35 If a mapping implements the the `apply_transpose` method this allows working with 35 If a mapping implements the the `apply_transpose` method this allows working with
36 the transpose of mapping `m` by using `m'`. `m'` will work as a regular LazyTensor lazily calling 36 the transpose of mapping `m` by using `m'`. `m'` will work as a regular LazyTensor lazily calling
37 the appropriate methods of `m`. 37 the appropriate methods of `m`.
38 """ 38 """
39 struct LazyTensorTranspose{T,R,D, TM<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R} 39 struct TensorTranspose{T,R,D, TM<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R}
40 tm::TM 40 tm::TM
41 end 41 end
42 42
43 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors? 43 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
44 # Jonatan 2020-09-25: Is the problem that you can take the transpose of any LazyTensor even if it doesn't implement `apply_transpose`? 44 # Jonatan 2020-09-25: Is the problem that you can take the transpose of any LazyTensor even if it doesn't implement `apply_transpose`?
45 Base.adjoint(tm::LazyTensor) = LazyTensorTranspose(tm) 45 Base.adjoint(tm::LazyTensor) = TensorTranspose(tm)
46 Base.adjoint(tmt::LazyTensorTranspose) = tmt.tm 46 Base.adjoint(tmt::TensorTranspose) = tmt.tm
47 47
48 apply(tmt::LazyTensorTranspose{T,R,D}, v::AbstractArray{<:Any,R}, I::Vararg{Any,D}) where {T,R,D} = apply_transpose(tmt.tm, v, I...) 48 apply(tmt::TensorTranspose{T,R,D}, v::AbstractArray{<:Any,R}, I::Vararg{Any,D}) where {T,R,D} = apply_transpose(tmt.tm, v, I...)
49 apply_transpose(tmt::LazyTensorTranspose{T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmt.tm, v, I...) 49 apply_transpose(tmt::TensorTranspose{T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmt.tm, v, I...)
50 50
51 range_size(tmt::LazyTensorTranspose) = domain_size(tmt.tm) 51 range_size(tmt::TensorTranspose) = domain_size(tmt.tm)
52 domain_size(tmt::LazyTensorTranspose) = range_size(tmt.tm) 52 domain_size(tmt::TensorTranspose) = range_size(tmt.tm)
53 53
54 54
55 struct LazyTensorBinaryOperation{Op,T,R,D,T1<:LazyTensor{T,R,D},T2<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R} 55 struct ElementwiseTensorOperation{Op,T,R,D,T1<:LazyTensor{T,R,D},T2<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R}
56 tm1::T1 56 tm1::T1
57 tm2::T2 57 tm2::T2
58 58
59 function LazyTensorBinaryOperation{Op,T,R,D}(tm1::T1,tm2::T2) where {Op,T,R,D, T1<:LazyTensor{T,R,D},T2<:LazyTensor{T,R,D}} 59 function ElementwiseTensorOperation{Op,T,R,D}(tm1::T1,tm2::T2) where {Op,T,R,D, T1<:LazyTensor{T,R,D},T2<:LazyTensor{T,R,D}}
60 @boundscheck check_domain_size(tm2, domain_size(tm1)) 60 @boundscheck check_domain_size(tm2, domain_size(tm1))
61 @boundscheck check_range_size(tm2, range_size(tm1)) 61 @boundscheck check_range_size(tm2, range_size(tm1))
62 return new{Op,T,R,D,T1,T2}(tm1,tm2) 62 return new{Op,T,R,D,T1,T2}(tm1,tm2)
63 end 63 end
64 end 64 end
65 65
66 LazyTensorBinaryOperation{Op}(s,t) where Op = LazyTensorBinaryOperation{Op,eltype(s), range_dim(s), domain_dim(s)}(s,t) 66 ElementwiseTensorOperation{Op}(s,t) where Op = ElementwiseTensorOperation{Op,eltype(s), range_dim(s), domain_dim(s)}(s,t)
67 67
68 apply(tmBinOp::LazyTensorBinaryOperation{:+,T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) + apply(tmBinOp.tm2, v, I...) 68 apply(tmBinOp::ElementwiseTensorOperation{:+,T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) + apply(tmBinOp.tm2, v, I...)
69 apply(tmBinOp::LazyTensorBinaryOperation{:-,T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) - apply(tmBinOp.tm2, v, I...) 69 apply(tmBinOp::ElementwiseTensorOperation{:-,T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) - apply(tmBinOp.tm2, v, I...)
70 70
71 range_size(tmBinOp::LazyTensorBinaryOperation) = range_size(tmBinOp.tm1) 71 range_size(tmBinOp::ElementwiseTensorOperation) = range_size(tmBinOp.tm1)
72 domain_size(tmBinOp::LazyTensorBinaryOperation) = domain_size(tmBinOp.tm1) 72 domain_size(tmBinOp::ElementwiseTensorOperation) = domain_size(tmBinOp.tm1)
73 73
74 74
75 """ 75 """
76 TensorComposition{T,R,K,D} 76 TensorComposition{T,R,K,D}
77 77