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annotate src/LazyTensors/lazy_tensor_operations.jl @ 379:de4746d6d126

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Add some notes and todos
author Jonatan Werpers <jonatan@werpers.com>
date Wed, 30 Sep 2020 21:30:29 +0200
parents 946516954c85
children 1936e38fe51e
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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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17 # TODO: Go through and remove unneccerary type parameters on functions
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19 Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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20 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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21 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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22 Base.size(ta::LazyTensorMappingApplication) = range_size(ta.t)
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23 # TODO: What else is needed to implement the AbstractArray interface?
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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 Base.:*(a::TensorMapping{T,R,D}, b::TensorMapping{T,D,K}, args::Union{TensorMapping{T}, AbstractArray{T}}...) where {T,R,D,K} = foldr(*,(a,b,args...))
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27 # # Should we overload some other infix binary opesrator?
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28 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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29 # TODO: We need to be really careful about good error messages.
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30 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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31
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32 """
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33 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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34
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35 Struct for lazy transpose of a TensorMapping.
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36
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37 If a mapping implements the the `apply_transpose` method this allows working with
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38 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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39 the appropriate methods of `m`.
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40 """
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41 struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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42 tm::TensorMapping{T,R,D}
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43 end
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44 export LazyTensorMappingTranspose
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45
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46 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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47 # 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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48 Base.adjoint(tm::TensorMapping) = LazyTensorMappingTranspose(tm)
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49 Base.adjoint(tmt::LazyTensorMappingTranspose) = tmt.tm
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51 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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52 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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53
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54 range_size(tmt::LazyTensorMappingTranspose) = domain_size(tmt.tm)
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55 domain_size(tmt::LazyTensorMappingTranspose) = range_size(tmt.tm)
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57
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58 struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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59 tm1::T1
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60 tm2::T2
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62 @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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63 return new{Op,T,R,D,T1,T2}(tm1,tm2)
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64 end
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65 end
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66 # TODO: Boundschecking in constructor.
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67
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68 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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69 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 range_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = range_size(tmBinOp.tm1)
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72 domain_size(tmBinOp::LazyTensorMappingBinaryOperation{Op,T,R,D}) where {Op,T,R,D} = domain_size(tmBinOp.tm1)
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73
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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 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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78 # TODO: Write tests and documentation for LazyTensorMappingComposition
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79 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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80 # t1::TensorMapping{T,R,K}
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81 # t2::TensorMapping{T,K,D}
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82 # end
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83
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84 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
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85
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86 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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87 # range_size(tm.t1, domain_size(tm.t2, domain_size))
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88 # end
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89
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90 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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91 # domain_size(tm.t1, domain_size(tm.t2, range_size))
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92 # end
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93
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94 # 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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95 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
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96 # end
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97
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98 # 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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99 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
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100 # end
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101
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102 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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103
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104 # export →