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annotate src/LazyTensors/lazy_tensor_operations.jl @ 547:53828d3ed132 refactor/tensor_index_coupling

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Remove unnecessary type parameters
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Sat, 28 Nov 2020 17:03:01 +0100
parents 62d96e2cd165
children 8f7919a9b398
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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 Base.getindex(ta::LazyTensorMappingApplication{T,R}, I::Vararg{Any,R}) where {T,R} = apply(ta.t, ta.o, I...)
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18 Base.size(ta::LazyTensorMappingApplication) = range_size(ta.t)
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19 # TODO: What else is needed to implement the AbstractArray interface?
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20
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21 Base.:*(a::TensorMapping, v::AbstractArray) = LazyTensorMappingApplication(a,v)
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22 Base.:*(a::TensorMapping, b::TensorMapping) = throw(MethodError(Base.:*,(a,b)))
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23 Base.:*(a::TensorMapping, args::Union{TensorMapping, AbstractArray}...) = foldr(*,(a,args...))
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24
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25 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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26 # # Should we overload some other infix binary opesrator?
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27 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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28 # TODO: We need to be really careful about good error messages.
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29 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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30
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31 """
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32 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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33
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34 Struct for lazy transpose of a TensorMapping.
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35
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36 If a mapping implements the the `apply_transpose` method this allows working with
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37 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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38 the appropriate methods of `m`.
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39 """
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40 struct LazyTensorMappingTranspose{T,R,D, TM<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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41 tm::TM
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42 end
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43 export LazyTensorMappingTranspose
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44
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45 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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46 # 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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47 Base.adjoint(tm::TensorMapping) = LazyTensorMappingTranspose(tm)
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48 Base.adjoint(tmt::LazyTensorMappingTranspose) = tmt.tm
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49
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50 apply(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Any,D}) where {T,R,D} = apply_transpose(tmt.tm, v, I...)
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51 apply_transpose(tmt::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmt.tm, v, I...)
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52
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53 range_size(tmt::LazyTensorMappingTranspose) = domain_size(tmt.tm)
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54 domain_size(tmt::LazyTensorMappingTranspose) = range_size(tmt.tm)
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56
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57 struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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58 tm1::T1
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59 tm2::T2
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61 @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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62 return new{Op,T,R,D,T1,T2}(tm1,tm2)
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63 end
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64 end
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65 # TODO: Boundschecking in constructor.
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66
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67 apply(tmBinOp::LazyTensorMappingBinaryOperation{:+,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) + apply(tmBinOp.tm2, v, I...)
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68 apply(tmBinOp::LazyTensorMappingBinaryOperation{:-,T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D} = apply(tmBinOp.tm1, v, I...) - apply(tmBinOp.tm2, v, I...)
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69
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70 range_size(tmBinOp::LazyTensorMappingBinaryOperation) = range_size(tmBinOp.tm1)
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71 domain_size(tmBinOp::LazyTensorMappingBinaryOperation) = domain_size(tmBinOp.tm1)
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72
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73 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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74 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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75
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76 """
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77 TensorMappingComposition{T,R,K,D}
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78
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79 Lazily compose two TensorMappings, so that they can be handled as a single TensorMapping.
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80 """
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81 struct TensorMappingComposition{T,R,K,D, TM1<:TensorMapping{T,R,K}, TM2<:TensorMapping{T,K,D}} <: TensorMapping{T,R,D}
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82 t1::TM1
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83 t2::TM2
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84
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85 @inline function TensorMappingComposition(t1::TensorMapping{T,R,K}, t2::TensorMapping{T,K,D}) where {T,R,K,D}
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86 @boundscheck check_domain_size(t1, range_size(t2))
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87 return new{T,R,K,D, typeof(t1), typeof(t2)}(t1,t2)
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88 end
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89 end
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90 export TensorMappingComposition
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91
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92 range_size(tm::TensorMappingComposition) = range_size(tm.t1)
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93 domain_size(tm::TensorMappingComposition) = domain_size(tm.t2)
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94
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95 function apply(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,K,D}
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96 apply(c.t1, c.t2*v, I...)
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97 end
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98
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99 function apply_transpose(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,R}, I::Vararg{Any,D}) where {T,R,K,D}
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100 apply_transpose(c.t2, c.t1'*v, I...)
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101 end
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102
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103 Base.@propagate_inbounds Base.:∘(s::TensorMapping, t::TensorMapping) = TensorMappingComposition(s,t)
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104
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105 """
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106 LazyLinearMap{T,R,D,...}(A, range_indicies, domain_indicies)
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107
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108 TensorMapping defined by the AbstractArray A. `range_indicies` and `domain_indicies` define which indicies of A should
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109 be considerd the range and domain of the TensorMapping. Each set of indices must be ordered in ascending order.
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110
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111 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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112 standard matrix-vector product on vectors of size n.
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113 """
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114 struct LazyLinearMap{T,R,D, RD, AA<:AbstractArray{T,RD}} <: TensorMapping{T,R,D}
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115 A::AA
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116 range_indicies::NTuple{R,Int}
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117 domain_indicies::NTuple{D,Int}
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118
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119 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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120 if !issorted(range_indicies) || !issorted(domain_indicies)
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121 throw(DomainError("range_indicies and domain_indicies must be sorted in ascending order"))
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122 end
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123
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124 return new{T,R,D,RD,AA}(A,range_indicies,domain_indicies)
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125 end
377
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126 end
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127 export LazyLinearMap
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128
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129 range_size(llm::LazyLinearMap) = size(llm.A)[[llm.range_indicies...]]
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130 domain_size(llm::LazyLinearMap) = size(llm.A)[[llm.domain_indicies...]]
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131
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132 function apply(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D}
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133 view_index = ntuple(i->:,ndims(llm.A))
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134 for i ∈ 1:R
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135 view_index = Base.setindex(view_index, Int(I[i]), llm.range_indicies[i])
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136 end
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137 A_view = @view llm.A[view_index...]
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138 return sum(A_view.*v)
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139 end
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140
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141 function apply_transpose(llm::LazyLinearMap{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Any,D}) where {T,R,D}
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142 apply(LazyLinearMap(llm.A, llm.domain_indicies, llm.range_indicies), v, I...)
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143 end
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144
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145
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146 """
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147 IdentityMapping{T,D} <: TensorMapping{T,D,D}
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148
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149 The lazy identity TensorMapping for a given size. Usefull for building up higher dimensional tensor mappings from lower
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150 dimensional ones through outer products. Also used in the Implementation for InflatedTensorMapping.
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151 """
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152 struct IdentityMapping{T,D} <: TensorMapping{T,D,D}
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153 size::NTuple{D,Int}
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154 end
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155 export IdentityMapping
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156
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157 IdentityMapping{T}(size::NTuple{D,Int}) where {T,D} = IdentityMapping{T,D}(size)
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158 IdentityMapping{T}(size::Vararg{Int,D}) where {T,D} = IdentityMapping{T,D}(size)
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159 IdentityMapping(size::Vararg{Int,D}) where D = IdentityMapping{Float64,D}(size)
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160
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161 range_size(tmi::IdentityMapping) = tmi.size
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162 domain_size(tmi::IdentityMapping) = tmi.size
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163
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164 apply(tmi::IdentityMapping{T,D}, v::AbstractArray{T,D}, I::Vararg{Any,D}) where {T,D} = v[I...]
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165 apply_transpose(tmi::IdentityMapping{T,D}, v::AbstractArray{T,D}, I::Vararg{Any,D}) where {T,D} = v[I...]
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166
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167 """
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168 Base.:∘(tm, tmi)
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169 Base.:∘(tmi, tm)
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170
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171 Composes a `Tensormapping` `tm` with an `IdentityMapping` `tmi`, by returning `tm`
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172 """
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173 @inline function Base.:∘(tm::TensorMapping{T,R,D}, tmi::IdentityMapping{T,D}) where {T,R,D}
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174 @boundscheck check_domain_size(tm, range_size(tmi))
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175 return tm
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176 end
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177
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178 @inline function Base.:∘(tmi::IdentityMapping{T,R}, tm::TensorMapping{T,R,D}) where {T,R,D}
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179 @boundscheck check_domain_size(tmi, range_size(tm))
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180 return tm
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181 end
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182 # Specialization for the case where tm is an IdentityMapping. Required to resolve ambiguity.
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183 @inline function Base.:∘(tm::IdentityMapping{T,D}, tmi::IdentityMapping{T,D}) where {T,D}
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184 @boundscheck check_domain_size(tm, range_size(tmi))
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185 return tmi
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186 end
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187
460
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188
459
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189 """
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190 InflatedTensorMapping{T,R,D} <: TensorMapping{T,R,D}
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191
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192 An inflated `TensorMapping` with dimensions added before and afer its actual dimensions.
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193 """
449
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194 struct InflatedTensorMapping{T,R,D,D_before,R_middle,D_middle,D_after, TM<:TensorMapping{T,R_middle,D_middle}} <: TensorMapping{T,R,D}
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195 before::IdentityMapping{T,D_before}
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196 tm::TM
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197 after::IdentityMapping{T,D_after}
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198
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199 function InflatedTensorMapping(before, tm::TensorMapping{T}, after) where T
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200 R_before = range_dim(before)
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201 R_middle = range_dim(tm)
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202 R_after = range_dim(after)
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203 R = R_before+R_middle+R_after
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204
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205 D_before = domain_dim(before)
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206 D_middle = domain_dim(tm)
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207 D_after = domain_dim(after)
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208 D = D_before+D_middle+D_after
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209 return new{T,R,D,D_before,R_middle,D_middle,D_after, typeof(tm)}(before, tm, after)
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210 end
446
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211 end
451
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212 export InflatedTensorMapping
461
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213 """
464
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214 InflatedTensorMapping(before, tm, after)
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215 InflatedTensorMapping(before,tm)
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216 InflatedTensorMapping(tm,after)
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217
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218 The outer product of `before`, `tm` and `after`, where `before` and `after` are `IdentityMapping`s.
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219
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220 If one of `before` or `after` is left out, a 0-dimensional `IdentityMapping` is used as the default value.
493
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221
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222 If `tm` already is an `InflatedTensorMapping`, `before` and `after` will be extended instead of
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223 creating a nested `InflatedTensorMapping`.
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224 """
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225 InflatedTensorMapping(::IdentityMapping, ::TensorMapping, ::IdentityMapping)
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226
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227 function InflatedTensorMapping(before, itm::InflatedTensorMapping, after)
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228 return InflatedTensorMapping(
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229 IdentityMapping(before.size..., itm.before.size...),
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230 itm.tm,
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231 IdentityMapping(itm.after.size..., after.size...),
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232 )
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233 end
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234
462
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diff changeset
235 InflatedTensorMapping(before::IdentityMapping, tm::TensorMapping{T}) where T = InflatedTensorMapping(before,tm,IdentityMapping{T}())
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236 InflatedTensorMapping(tm::TensorMapping{T}, after::IdentityMapping) where T = InflatedTensorMapping(IdentityMapping{T}(),tm,after)
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237 # Resolve ambiguity between the two previous methods
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238 InflatedTensorMapping(I1::IdentityMapping{T}, I2::IdentityMapping{T}) where T = InflatedTensorMapping(I1,I2,IdentityMapping{T}())
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239
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240 # TODO: Implement some pretty printing in terms of ⊗. E.g InflatedTensorMapping(I(3),B,I(2)) -> I(3)⊗B⊗I(2)
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241
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242 function range_size(itm::InflatedTensorMapping)
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243 return flatten_tuple(
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244 range_size(itm.before),
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245 range_size(itm.tm),
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246 range_size(itm.after),
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247 )
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248 end
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249
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250 function domain_size(itm::InflatedTensorMapping)
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251 return flatten_tuple(
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252 domain_size(itm.before),
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253 domain_size(itm.tm),
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254 domain_size(itm.after),
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255 )
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256 end
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257
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258 function apply(itm::InflatedTensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg{Any,R}) where {T,R,D}
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259 dim_before = range_dim(itm.before)
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260 dim_domain = domain_dim(itm.tm)
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261 dim_range = range_dim(itm.tm)
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262 dim_after = range_dim(itm.after)
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263
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264 view_index, inner_index = split_index(Val(dim_before), Val(dim_domain), Val(dim_range), Val(dim_after), I...)
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265
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266 v_inner = view(v, view_index...)
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267 return apply(itm.tm, v_inner, inner_index...)
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268 end
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269
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270 function apply_transpose(itm::InflatedTensorMapping{T,R,D}, v::AbstractArray{T,R}, I::Vararg{Any,D}) where {T,R,D}
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271 dim_before = range_dim(itm.before)
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272 dim_domain = domain_dim(itm.tm)
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273 dim_range = range_dim(itm.tm)
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274 dim_after = range_dim(itm.after)
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275
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276 view_index, inner_index = split_index(Val(dim_before), Val(dim_range), Val(dim_domain), Val(dim_after), I...)
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277
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278 v_inner = view(v, view_index...)
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279 return apply_transpose(itm.tm, v_inner, inner_index...)
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280 end
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281
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282
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283 """
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284 split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...)
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285
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286 Splits the multi-index `I` into two parts. One part which is expected to be
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287 used as a view, and one which is expected to be used as an index.
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288 Eg.
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289 ```
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290 split_index(Val(1),Val(3),Val(2),Val(1),(1,2,3,4)) -> (1,:,:,:,4), (2,3)
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291 ```
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292
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293 `dim_view` controls how many colons are in the view, and `dim_index` controls
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294 how many elements are extracted from the middle.
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295 `dim_before` and `dim_after` decides the length of the index parts before and after the colons in the view index.
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296
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297 Arguments should satisfy `length(I) == dim_before+B_domain+dim_after`.
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298
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299 The returned values satisfy
533
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300 * `length(view_index) == dim_before + dim_view + dim_after`
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301 * `length(I_middle) == dim_index`
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302 """
533
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303 function split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...) where {dim_before,dim_view, dim_index,dim_after}
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304 I_before, I_middle, I_after = split_tuple(I, Val(dim_before), Val(dim_index))
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305
533
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306 view_index = (I_before..., ntuple((i)->:, dim_view)..., I_after...)
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307
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308 return view_index, I_middle
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309 end
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310
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311 # TODO: Can this be replaced by something more elegant while still being type stable? 2020-10-21
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312 # See:
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313 # https://github.com/JuliaLang/julia/issues/34884
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314 # https://github.com/JuliaLang/julia/issues/30386
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315 """
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316 slice_tuple(t, Val(l), Val(u))
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317
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318 Get a slice of a tuple in a type stable way.
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319 Equivalent to t[l:u] but type stable.
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320 """
455
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321 function slice_tuple(t,::Val{L},::Val{U}) where {L,U}
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322 return ntuple(i->t[i+L-1], U-L+1)
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323 end
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324
457
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325 """
532
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326 split_tuple(t::Tuple{...}, ::Val{M}) where {N,M}
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327
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328 Split the tuple `t` into two parts. the first part is `M` long.
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329 E.g
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330 ```
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331 split_tuple((1,2,3,4),Val(3)) -> (1,2,3), (4,)
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332 ```
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333 """
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334 function split_tuple(t::NTuple{N},::Val{M}) where {N,M}
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335 return slice_tuple(t,Val(1), Val(M)), slice_tuple(t,Val(M+1), Val(N))
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336 end
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337
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338 """
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339 split_tuple(t::Tuple{...},::Val{M},::Val{K}) where {N,M,K}
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340
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341 Same as `split_tuple(t::NTuple{N},::Val{M})` but splits the tuple in three parts. With the first
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342 two parts having lenght `M` and `K`.
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343 """
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344 function split_tuple(t::NTuple{N},::Val{M},::Val{K}) where {N,M,K}
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345 p1, tail = split_tuple(t, Val(M))
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346 p2, p3 = split_tuple(tail, Val(K))
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347 return p1,p2,p3
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348 end
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349
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350
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351 """
457
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352 flatten_tuple(t)
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353
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354 Takes a nested tuple and flattens the whole structure
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355 """
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356 flatten_tuple(t::NTuple{N, Number} where N) = t
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357 flatten_tuple(t::Tuple) = ((flatten_tuple.(t)...)...,) # simplify?
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358 flatten_tuple(ts::Vararg) = flatten_tuple(ts)
487
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359
468
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360 """
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361 LazyOuterProduct(tms...)
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362
501
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363 Creates a `TensorMappingComposition` for the outerproduct of `tms...`.
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364 This is done by separating the outer product into regular products of outer products involving only identity mappings and one non-identity mapping.
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365
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366 First let
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367 ```math
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368 A = A_{I,J}
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369 B = B_{M,N}
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370 C = C_{P,Q}
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371 ```
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372
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373 where ``I``, ``M``, ``P`` are multi-indexes for the ranges of ``A``, ``B``, ``C``, and ``J``, ``N``, ``Q`` are multi-indexes of the domains.
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374
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375 We use ``⊗`` to denote the outer product
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376 ```math
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377 (A⊗B)_{IM,JN} = A_{I,J}B_{M,N}
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378 ```
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379
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380 We note that
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381 ```math
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382 A⊗B⊗C = (A⊗B⊗C)_{IMP,JNQ} = A_{I,J}B_{M,N}C_{P,Q}
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383 ```
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384 And that
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385 ```math
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386 A⊗B⊗C = (A⊗I_{|M|}⊗I_{|P|})(I_{|J|}⊗B⊗I_{|P|})(I_{|J|}⊗I_{|N|}⊗C)
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387 ```
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388 where |.| of a multi-index is a vector of sizes for each dimension. ``I_v`` denotes the identity tensor of size ``v[i]`` in each direction
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389 To apply ``A⊗B⊗C`` we evaluate
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390
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391 (A⊗B⊗C)v = [(A⊗I_{|M|}⊗I_{|P|}) [(I_{|J|}⊗B⊗I_{|P|}) [(I_{|J|}⊗I_{|N|}⊗C)v]]]
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392 """
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393 function LazyOuterProduct end
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394 export LazyOuterProduct
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395
470
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396 function LazyOuterProduct(tm1::TensorMapping{T}, tm2::TensorMapping{T}) where T
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397 itm1 = InflatedTensorMapping(tm1, IdentityMapping{T}(range_size(tm2)))
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398 itm2 = InflatedTensorMapping(IdentityMapping{T}(domain_size(tm1)),tm2)
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399
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400 return itm1∘itm2
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401 end
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402
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403 LazyOuterProduct(t1::IdentityMapping{T}, t2::IdentityMapping{T}) where T = IdentityMapping{T}(t1.size...,t2.size...)
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404 LazyOuterProduct(t1::TensorMapping, t2::IdentityMapping) = InflatedTensorMapping(t1, t2)
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405 LazyOuterProduct(t1::IdentityMapping, t2::TensorMapping) = InflatedTensorMapping(t1, t2)
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406
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407 LazyOuterProduct(tms::Vararg{TensorMapping}) = foldl(LazyOuterProduct, tms)
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408
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409 ⊗(a::TensorMapping, b::TensorMapping) = LazyOuterProduct(a,b)
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410 export ⊗
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411
498
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412
487
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413 function check_domain_size(tm::TensorMapping, sz)
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414 if domain_size(tm) != sz
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415 throw(SizeMismatch(tm,sz))
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416 end
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417 end
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418
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419 struct SizeMismatch <: Exception
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420 tm::TensorMapping
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421 sz
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422 end
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423 export SizeMismatch
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424
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425 function Base.showerror(io::IO, err::SizeMismatch)
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426 print(io, "SizeMismatch: ")
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427 print(io, "domain size $(domain_size(err.tm)) of TensorMapping not matching size $(err.sz)")
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428 end