Mercurial > repos > public > sbplib_julia
annotate src/LazyTensors/lazy_tensor_operations.jl @ 1007:f7a718bcb4da refactor/lazy_tensors
Add checking of sizes to LazyTensorBinaryOperation
| author | Jonatan Werpers <jonatan@werpers.com> |
|---|---|
| date | Sun, 20 Mar 2022 22:41:28 +0100 |
| parents | d9476fede83d |
| children | 2c1a0722ddb9 4dd3c2312d9e 52f07c77299d |
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1 """ |
| 995 | 2 LazyTensorApplication{T,R,D} <: LazyArray{T,R} |
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3 |
| 995 | 4 Struct for lazy application of a LazyTensor. Created using `*`. |
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5 |
| 995 | 6 Allows the result of a `LazyTensor` applied to a vector to be treated as an `AbstractArray`. |
| 7 With a mapping `m` and a vector `v` the LazyTensorApplication 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 """ |
| 995 | 10 struct LazyTensorApplication{T,R,D, TM<:LazyTensor{<:Any,R,D}, AA<:AbstractArray{<:Any,D}} <: LazyArray{T,R} |
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11 t::TM |
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12 o::AA |
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13 |
| 995 | 14 function LazyTensorApplication(t::LazyTensor{<:Any,R,D}, o::AbstractArray{<:Any,D}) where {R,D} |
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15 @boundscheck check_domain_size(t, size(o)) |
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16 I = ntuple(i->1, range_dim(t)) |
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17 T = typeof(apply(t,o,I...)) |
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18 return new{T,R,D,typeof(t), typeof(o)}(t,o) |
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19 end |
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20 end |
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21 |
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22 function Base.getindex(ta::LazyTensorApplication{T,R}, I::Vararg{Any,R}) where {T,R} |
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23 @boundscheck checkbounds(ta, Int.(I)...) |
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24 return apply(ta.t, ta.o, I...) |
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25 end |
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26 Base.getindex(ta::LazyTensorApplication{T,1} where T, I::CartesianIndex{1}) = ta[Tuple(I)...] # Would otherwise be caught in the previous method. |
| 995 | 27 Base.size(ta::LazyTensorApplication) = range_size(ta.t) |
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28 |
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29 |
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30 """ |
| 995 | 31 LazyTensorTranspose{T,R,D} <: LazyTensor{T,D,R} |
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32 |
| 995 | 33 Struct for lazy transpose of a LazyTensor. |
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34 |
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35 If a mapping implements the the `apply_transpose` method this allows working with |
| 995 | 36 the transpose of mapping `m` by using `m'`. `m'` will work as a regular LazyTensor lazily calling |
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37 the appropriate methods of `m`. |
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38 """ |
| 995 | 39 struct LazyTensorTranspose{T,R,D, TM<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R} |
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40 tm::TM |
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41 end |
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42 |
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43 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors? |
| 995 | 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) | |
| 46 Base.adjoint(tmt::LazyTensorTranspose) = tmt.tm | |
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47 |
| 995 | 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...) |
| 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...) | |
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50 |
| 995 | 51 range_size(tmt::LazyTensorTranspose) = domain_size(tmt.tm) |
| 52 domain_size(tmt::LazyTensorTranspose) = range_size(tmt.tm) | |
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53 |
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54 |
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55 struct LazyTensorBinaryOperation{Op,T,R,D,T1<:LazyTensor{T,R,D},T2<:LazyTensor{T,R,D}} <: LazyTensor{T,D,R} |
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56 tm1::T1 |
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57 tm2::T2 |
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58 |
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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}} |
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60 @boundscheck check_domain_size(tm2, domain_size(tm1)) |
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61 @boundscheck check_range_size(tm2, range_size(tm1)) |
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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 |
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66 LazyTensorBinaryOperation{Op}(s,t) where Op = LazyTensorBinaryOperation{Op,eltype(s), range_dim(s), domain_dim(s)}(s,t) |
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67 |
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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...) |
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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...) |
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70 |
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71 range_size(tmBinOp::LazyTensorBinaryOperation) = range_size(tmBinOp.tm1) |
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72 domain_size(tmBinOp::LazyTensorBinaryOperation) = domain_size(tmBinOp.tm1) |
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73 |
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74 |
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75 """ |
| 995 | 76 LazyTensorComposition{T,R,K,D} |
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77 |
| 995 | 78 Lazily compose two `LazyTensor`s, so that they can be handled as a single `LazyTensor`. |
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79 """ |
| 995 | 80 struct LazyTensorComposition{T,R,K,D, TM1<:LazyTensor{T,R,K}, TM2<:LazyTensor{T,K,D}} <: LazyTensor{T,R,D} |
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81 t1::TM1 |
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82 t2::TM2 |
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83 |
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84 function LazyTensorComposition(t1::LazyTensor{T,R,K}, t2::LazyTensor{T,K,D}) where {T,R,K,D} |
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85 @boundscheck check_domain_size(t1, range_size(t2)) |
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86 return new{T,R,K,D, typeof(t1), typeof(t2)}(t1,t2) |
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87 end |
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88 end |
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89 |
| 995 | 90 range_size(tm::LazyTensorComposition) = range_size(tm.t1) |
| 91 domain_size(tm::LazyTensorComposition) = domain_size(tm.t2) | |
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92 |
| 995 | 93 function apply(c::LazyTensorComposition{T,R,K,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,K,D} |
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94 apply(c.t1, c.t2*v, I...) |
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95 end |
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96 |
| 995 | 97 function apply_transpose(c::LazyTensorComposition{T,R,K,D}, v::AbstractArray{<:Any,R}, I::Vararg{Any,D}) where {T,R,K,D} |
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98 apply_transpose(c.t2, c.t1'*v, I...) |
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99 end |
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100 |
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101 |
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102 """ |
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103 LazyTensorComposition(tm, tmi::IdentityTensor) |
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104 LazyTensorComposition(tmi::IdentityTensor, tm) |
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105 |
| 995 | 106 Composes a `Tensormapping` `tm` with an `IdentityTensor` `tmi`, by returning `tm` |
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107 """ |
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108 function LazyTensorComposition(tm::LazyTensor{T,R,D}, tmi::IdentityTensor{T,D}) where {T,R,D} |
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109 @boundscheck check_domain_size(tm, range_size(tmi)) |
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110 return tm |
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111 end |
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112 |
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113 function LazyTensorComposition(tmi::IdentityTensor{T,R}, tm::LazyTensor{T,R,D}) where {T,R,D} |
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114 @boundscheck check_domain_size(tmi, range_size(tm)) |
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115 return tm |
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116 end |
| 995 | 117 # Specialization for the case where tm is an IdentityTensor. Required to resolve ambiguity. |
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118 function LazyTensorComposition(tm::IdentityTensor{T,D}, tmi::IdentityTensor{T,D}) where {T,D} |
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119 @boundscheck check_domain_size(tm, range_size(tmi)) |
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120 return tmi |
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121 end |
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122 |
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123 |
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124 """ |
| 995 | 125 InflatedLazyTensor{T,R,D} <: LazyTensor{T,R,D} |
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126 |
| 995 | 127 An inflated `LazyTensor` with dimensions added before and afer its actual dimensions. |
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128 """ |
| 995 | 129 struct InflatedLazyTensor{T,R,D,D_before,R_middle,D_middle,D_after, TM<:LazyTensor{T,R_middle,D_middle}} <: LazyTensor{T,R,D} |
| 130 before::IdentityTensor{T,D_before} | |
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131 tm::TM |
| 995 | 132 after::IdentityTensor{T,D_after} |
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133 |
| 995 | 134 function InflatedLazyTensor(before, tm::LazyTensor{T}, after) where T |
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135 R_before = range_dim(before) |
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136 R_middle = range_dim(tm) |
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137 R_after = range_dim(after) |
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138 R = R_before+R_middle+R_after |
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139 |
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140 D_before = domain_dim(before) |
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141 D_middle = domain_dim(tm) |
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142 D_after = domain_dim(after) |
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143 D = D_before+D_middle+D_after |
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144 return new{T,R,D,D_before,R_middle,D_middle,D_after, typeof(tm)}(before, tm, after) |
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145 end |
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146 end |
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147 |
| 461 | 148 """ |
| 995 | 149 InflatedLazyTensor(before, tm, after) |
| 150 InflatedLazyTensor(before,tm) | |
| 151 InflatedLazyTensor(tm,after) | |
| 461 | 152 |
| 995 | 153 The outer product of `before`, `tm` and `after`, where `before` and `after` are `IdentityTensor`s. |
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154 |
| 995 | 155 If one of `before` or `after` is left out, a 0-dimensional `IdentityTensor` is used as the default value. |
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156 |
| 995 | 157 If `tm` already is an `InflatedLazyTensor`, `before` and `after` will be extended instead of |
| 158 creating a nested `InflatedLazyTensor`. | |
| 461 | 159 """ |
| 995 | 160 InflatedLazyTensor(::IdentityTensor, ::LazyTensor, ::IdentityTensor) |
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161 |
| 995 | 162 function InflatedLazyTensor(before, itm::InflatedLazyTensor, after) |
| 163 return InflatedLazyTensor( | |
| 164 IdentityTensor(before.size..., itm.before.size...), | |
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165 itm.tm, |
| 995 | 166 IdentityTensor(itm.after.size..., after.size...), |
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167 ) |
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168 end |
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169 |
| 995 | 170 InflatedLazyTensor(before::IdentityTensor, tm::LazyTensor{T}) where T = InflatedLazyTensor(before,tm,IdentityTensor{T}()) |
| 171 InflatedLazyTensor(tm::LazyTensor{T}, after::IdentityTensor) where T = InflatedLazyTensor(IdentityTensor{T}(),tm,after) | |
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172 # Resolve ambiguity between the two previous methods |
| 995 | 173 InflatedLazyTensor(I1::IdentityTensor{T}, I2::IdentityTensor{T}) where T = InflatedLazyTensor(I1,I2,IdentityTensor{T}()) |
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174 |
| 995 | 175 # TODO: Implement some pretty printing in terms of ⊗. E.g InflatedLazyTensor(I(3),B,I(2)) -> I(3)⊗B⊗I(2) |
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176 |
| 995 | 177 function range_size(itm::InflatedLazyTensor) |
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178 return flatten_tuple( |
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179 range_size(itm.before), |
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180 range_size(itm.tm), |
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181 range_size(itm.after), |
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182 ) |
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183 end |
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184 |
| 995 | 185 function domain_size(itm::InflatedLazyTensor) |
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186 return flatten_tuple( |
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187 domain_size(itm.before), |
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188 domain_size(itm.tm), |
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189 domain_size(itm.after), |
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190 ) |
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191 end |
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192 |
| 995 | 193 function apply(itm::InflatedLazyTensor{T,R,D}, v::AbstractArray{<:Any,D}, I::Vararg{Any,R}) where {T,R,D} |
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194 dim_before = range_dim(itm.before) |
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195 dim_domain = domain_dim(itm.tm) |
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196 dim_range = range_dim(itm.tm) |
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197 dim_after = range_dim(itm.after) |
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198 |
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199 view_index, inner_index = split_index(Val(dim_before), Val(dim_domain), Val(dim_range), Val(dim_after), I...) |
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200 |
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201 v_inner = view(v, view_index...) |
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202 return apply(itm.tm, v_inner, inner_index...) |
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203 end |
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204 |
| 995 | 205 function apply_transpose(itm::InflatedLazyTensor{T,R,D}, v::AbstractArray{<:Any,R}, I::Vararg{Any,D}) where {T,R,D} |
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206 dim_before = range_dim(itm.before) |
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207 dim_domain = domain_dim(itm.tm) |
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208 dim_range = range_dim(itm.tm) |
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209 dim_after = range_dim(itm.after) |
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210 |
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211 view_index, inner_index = split_index(Val(dim_before), Val(dim_range), Val(dim_domain), Val(dim_after), I...) |
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212 |
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213 v_inner = view(v, view_index...) |
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214 return apply_transpose(itm.tm, v_inner, inner_index...) |
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215 end |
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216 |
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217 |
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218 @doc raw""" |
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219 LazyOuterProduct(tms...) |
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220 |
| 995 | 221 Creates a `LazyTensorComposition` for the outerproduct of `tms...`. |
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222 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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223 |
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224 First let |
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225 ```math |
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226 \begin{aligned} |
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227 A &= A_{I,J} \\ |
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228 B &= B_{M,N} \\ |
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229 C &= C_{P,Q} \\ |
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230 \end{aligned} |
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231 ``` |
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232 |
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233 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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234 |
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235 We use ``⊗`` to denote the outer product |
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236 ```math |
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237 (A⊗B)_{IM,JN} = A_{I,J}B_{M,N} |
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238 ``` |
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239 |
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240 We note that |
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241 ```math |
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242 A⊗B⊗C = (A⊗B⊗C)_{IMP,JNQ} = A_{I,J}B_{M,N}C_{P,Q} |
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243 ``` |
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244 And that |
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245 ```math |
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246 A⊗B⊗C = (A⊗I_{|M|}⊗I_{|P|})(I_{|J|}⊗B⊗I_{|P|})(I_{|J|}⊗I_{|N|}⊗C) |
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247 ``` |
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248 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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249 To apply ``A⊗B⊗C`` we evaluate |
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250 |
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251 ```math |
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252 (A⊗B⊗C)v = [(A⊗I_{|M|}⊗I_{|P|}) [(I_{|J|}⊗B⊗I_{|P|}) [(I_{|J|}⊗I_{|N|}⊗C)v]]] |
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253 ``` |
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254 """ |
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255 function LazyOuterProduct end |
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256 |
| 995 | 257 function LazyOuterProduct(tm1::LazyTensor{T}, tm2::LazyTensor{T}) where T |
| 258 itm1 = InflatedLazyTensor(tm1, IdentityTensor{T}(range_size(tm2))) | |
| 259 itm2 = InflatedLazyTensor(IdentityTensor{T}(domain_size(tm1)),tm2) | |
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260 |
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261 return itm1∘itm2 |
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263 |
| 995 | 264 LazyOuterProduct(t1::IdentityTensor{T}, t2::IdentityTensor{T}) where T = IdentityTensor{T}(t1.size...,t2.size...) |
| 265 LazyOuterProduct(t1::LazyTensor, t2::IdentityTensor) = InflatedLazyTensor(t1, t2) | |
| 266 LazyOuterProduct(t1::IdentityTensor, t2::LazyTensor) = InflatedLazyTensor(t1, t2) | |
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267 |
| 995 | 268 LazyOuterProduct(tms::Vararg{LazyTensor}) = foldl(LazyOuterProduct, tms) |
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| 498 | 270 |
| 995 | 271 function check_domain_size(tm::LazyTensor, sz) |
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272 if domain_size(tm) != sz |
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273 throw(DomainSizeMismatch(tm,sz)) |
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274 end |
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277 function check_range_size(tm::LazyTensor, sz) |
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278 if range_size(tm) != sz |
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279 throw(RangeSizeMismatch(tm,sz)) |
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280 end |
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281 end |
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282 |
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283 struct DomainSizeMismatch <: Exception |
| 995 | 284 tm::LazyTensor |
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285 sz |
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286 end |
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287 |
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288 function Base.showerror(io::IO, err::DomainSizeMismatch) |
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289 print(io, "DomainSizeMismatch: ") |
| 995 | 290 print(io, "domain size $(domain_size(err.tm)) of LazyTensor not matching size $(err.sz)") |
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291 end |
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293 |
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294 struct RangeSizeMismatch <: Exception |
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295 tm::LazyTensor |
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296 sz |
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297 end |
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299 function Base.showerror(io::IO, err::RangeSizeMismatch) |
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300 print(io, "RangeSizeMismatch: ") |
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301 print(io, "range size $(range_size(err.tm)) of LazyTensor not matching size $(err.sz)") |
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302 end |