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annotate src/LazyTensors/lazy_tensor_operations.jl @ 421:2808c41f5efa bugfix/tensor_application_multiplication

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Change defs of * for TensorApplication to fix bug
author Jonatan Werpers <jonatan@werpers.com>
date Sun, 18 Oct 2020 22:09:17 +0200
parents 4aa59af074ef
children cffdac9c612d
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1 """
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2 LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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3
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4 Struct for lazy application of a TensorMapping. Created using `*`.
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5
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6 Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
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7 With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
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8 The actual result will be calcualted when indexing into `m*v`.
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9 """
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10 struct LazyTensorMappingApplication{T,R,D, TM<:TensorMapping{T,R,D}, AA<:AbstractArray{T,D}} <: LazyArray{T,R}
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11 t::TM
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12 o::AA
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13 end
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14 # TODO: Do boundschecking on creation!
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15 export LazyTensorMappingApplication
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16
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17 # TODO: Go through and remove unneccerary type parameters on functions
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19 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Index,R}) where {T,R,D} = apply(ta.t, ta.o, I...)
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20 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Int,R}) where {T,R,D} = apply(ta.t, ta.o, Index{Unknown}.(I)...)
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21 Base.size(ta::LazyTensorMappingApplication) = range_size(ta.t)
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22 # TODO: What else is needed to implement the AbstractArray interface?
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23
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24 Base.:*(a::TensorMapping, v::AbstractArray) = LazyTensorMappingApplication(a,v)
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25 Base.:*(a::TensorMapping, b::TensorMapping) = throw(MethodError(Base.:*,(a,b)))
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26 Base.:*(a::TensorMapping, args::Union{TensorMapping, AbstractArray}...) = foldr(*,(a,args...))
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27
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28 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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29 # # Should we overload some other infix binary opesrator?
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30 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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31 # TODO: We need to be really careful about good error messages.
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32 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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33
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34 """
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35 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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36
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37 Struct for lazy transpose of a TensorMapping.
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38
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39 If a mapping implements the the `apply_transpose` method this allows working with
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40 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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41 the appropriate methods of `m`.
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42 """
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43 struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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44 tm::TensorMapping{T,R,D}
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45 end
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46 export LazyTensorMappingTranspose
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47
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48 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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49 # Jonatan 2020-09-25: Is the problem that you can take the transpose of any TensorMapping even if it doesn't implement `apply_transpose`?
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50 Base.adjoint(tm::TensorMapping) = LazyTensorMappingTranspose(tm)
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51 Base.adjoint(tmt::LazyTensorMappingTranspose) = tmt.tm
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52
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53 apply(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Index,D}) where {T,R,D} = apply_transpose(tmt.tm, v, I...)
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54 apply_transpose(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmt.tm, v, I...)
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55
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56 range_size(tmt::LazyTensorMappingTranspose) = domain_size(tmt.tm)
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57 domain_size(tmt::LazyTensorMappingTranspose) = range_size(tmt.tm)
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59
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60 struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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61 tm1::T1
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62 tm2::T2
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64 @inline function LazyTensorMappingBinaryOperation{Op,T,R,D}(tm1::T1,tm2::T2) where {Op,T,R,D, T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}}
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65 return new{Op,T,R,D,T1,T2}(tm1,tm2)
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66 end
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67 end
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68 # TODO: Boundschecking in constructor.
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69
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70 apply(tmBinOp::LazyTensorMappingBinaryOperation{:+,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) + apply(tmBinOp.tm2, v, I...)
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71 apply(tmBinOp::LazyTensorMappingBinaryOperation{:-,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) - apply(tmBinOp.tm2, v, I...)
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72
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73 range_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = range_size(tmBinOp.tm1)
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74 domain_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = domain_size(tmBinOp.tm1)
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75
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76 Base.:+(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(tm1,tm2)
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77 Base.:-(tm1::TensorMapping{T,R,D}, tm2::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(tm1,tm2)
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79
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80 # TODO: Write tests and documentation for LazyTensorMappingComposition
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81 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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82 # t1::TensorMapping{T,R,K}
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83 # t2::TensorMapping{T,K,D}
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84 # end
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85
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86 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
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87
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88 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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89 # range_size(tm.t1, domain_size(tm.t2, domain_size))
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90 # end
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91
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92 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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93 # domain_size(tm.t1, domain_size(tm.t2, range_size))
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94 # end
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95
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96 # function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,K,D}
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97 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
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98 # end
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99
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100 # function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{D,Int}) where {T,R,K,D}
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101 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
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102 # end
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103
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104 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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105
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106 # export →
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107 """
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108 LazyLinearMap{T,R,D,...}(A, range_indicies, domain_indicies)
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109
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110 TensorMapping defined by the AbstractArray A. `range_indicies` and `domain_indicies` define which indicies of A should
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111 be considerd the range and domain of the TensorMapping. Each set of indices must be ordered in ascending order.
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112
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113 For instance, if A is a m x n matrix, and range_size = (1,), domain_size = (2,), then the LazyLinearMap performs the
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114 standard matrix-vector product on vectors of size n.
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115 """
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116 struct LazyLinearMap{T,R,D, RD, AA<:AbstractArray{T,RD}} <: TensorMapping{T,R,D}
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117 A::AA
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118 range_indicies::NTuple{R,Int}
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119 domain_indicies::NTuple{D,Int}
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121 function LazyLinearMap(A::AA, range_indicies::NTuple{R,Int}, domain_indicies::NTuple{D,Int}) where {T,R,D, RD, AA<:AbstractArray{T,RD}}
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122 if !issorted(range_indicies) || !issorted(domain_indicies)
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123 throw(DomainError("range_indicies and domain_indicies must be sorted in ascending order"))
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124 end
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125
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126 return new{T,R,D,RD,AA}(A,range_indicies,domain_indicies)
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127 end
377
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128 end
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129 export LazyLinearMap
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130
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131 range_size(llm::LazyLinearMap) = size(llm.A)[[llm.range_indicies...]]
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132 domain_size(llm::LazyLinearMap) = size(llm.A)[[llm.domain_indicies...]]
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133
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134 function apply(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Index,R}) where {T,R,D}
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135 view_index = ntuple(i->:,ndims(llm.A))
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136 for i ∈ 1:R
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137 view_index = Base.setindex(view_index, Int(I[i]), llm.range_indicies[i])
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138 end
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139 A_view = @view llm.A[view_index...]
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140 return sum(A_view.*v)
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141 end
411
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142
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143 function apply_transpose(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Index,D}) where {T,R,D}
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144 apply(LazyLinearMap(llm.A, llm.domain_indicies, llm.range_indicies), v, I...)
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145 end