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annotate test/LazyTensors/lazy_tensor_operations_test.jl @ 955:43fa42c0906e bugfix/tensor_application_1d_cartesian_index

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author Jonatan Werpers <jonatan@werpers.com>
date Mon, 14 Mar 2022 08:36:40 +0100
parents 4a9a96d51940
children 86889fc5b63f
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1 using Test
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2 using Sbplib.LazyTensors
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3 using Sbplib.RegionIndices
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5 using Tullio
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6
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7 @testset "Mapping transpose" begin
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8 struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end
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10 LazyTensors.apply(m::DummyMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = :apply
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11 LazyTensors.apply_transpose(m::DummyMapping{T,R,D}, v, I::Vararg{Any,D}) where {T,R,D} = :apply_transpose
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12
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13 LazyTensors.range_size(m::DummyMapping) = :range_size
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14 LazyTensors.domain_size(m::DummyMapping) = :domain_size
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15
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16 m = DummyMapping{Float64,2,3}()
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17 @test m' isa TensorMapping{Float64, 3,2}
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18 @test m'' == m
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19 @test apply(m',zeros(Float64,(0,0)), 0, 0, 0) == :apply_transpose
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20 @test apply(m'',zeros(Float64,(0,0,0)), 0, 0) == :apply
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21 @test apply_transpose(m', zeros(Float64,(0,0,0)), 0, 0) == :apply
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22
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23 @test range_size(m') == :domain_size
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24 @test domain_size(m') == :range_size
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25 end
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26
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27 @testset "TensorApplication" begin
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28 struct SizeDoublingMapping{T,R,D} <: TensorMapping{T,R,D}
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29 domain_size::NTuple{D,Int}
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30 end
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31
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32 LazyTensors.apply(m::SizeDoublingMapping{T,R}, v, i::Vararg{Any,R}) where {T,R} = (:apply,v,i)
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33 LazyTensors.range_size(m::SizeDoublingMapping) = 2 .* m.domain_size
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34 LazyTensors.domain_size(m::SizeDoublingMapping) = m.domain_size
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36
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37 m = SizeDoublingMapping{Int, 1, 1}((3,))
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38 v = [0,1,2]
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39 @test m*v isa AbstractVector{Int}
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40 @test size(m*v) == 2 .*size(v)
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41 @test (m*v)[0] == (:apply,v,(0,))
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42 @test m*m*v isa AbstractVector{Int}
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43 @test (m*m*v)[1] == (:apply,m*v,(1,))
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44 @test (m*m*v)[3] == (:apply,m*v,(3,))
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45 @test (m*m*v)[6] == (:apply,m*v,(6,))
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46 @test_broken BoundsError == (m*m*v)[0]
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47 @test_broken BoundsError == (m*m*v)[7]
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48 @test_throws MethodError m*m
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49
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50 @test (m*v)[CartesianIndex(2)] == (:apply,v,(2,))
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51 @test (m*m*v)[CartesianIndex(2)] == (:apply,m*v,(2,))
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52
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53 m = SizeDoublingMapping{Int, 2, 1}((3,))
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54 @test_throws MethodError m*ones(Int,2,2)
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55 @test_throws MethodError m*m*v
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56
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57 m = SizeDoublingMapping{Float64, 2, 2}((3,3))
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58 v = ones(3,3)
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59 @test size(m*v) == 2 .*size(v)
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60 @test (m*v)[1,2] == (:apply,v,(1,2))
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61
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62 @test (m*v)[CartesianIndex(2,3)] == (:apply,v,(2,3))
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63 @test (m*m*v)[CartesianIndex(4,3)] == (:apply,m*v,(4,3))
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64
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65 struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
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66 λ::T
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67 size::NTuple{D,Int}
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68 end
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69
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70 LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
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71 LazyTensors.range_size(m::ScalingOperator) = m.size
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72 LazyTensors.domain_size(m::ScalingOperator) = m.size
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73
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74 m = ScalingOperator{Int,1}(2,(3,))
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75 v = [1,2,3]
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76 @test m*v isa AbstractVector
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77 @test m*v == [2,4,6]
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78
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79 m = ScalingOperator{Int,2}(2,(2,2))
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80 v = [[1 2];[3 4]]
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81 @test m*v == [[2 4];[6 8]]
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82 @test (m*v)[2,1] == 6
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83 end
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84
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85 @testset "TensorMapping binary operations" begin
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86 struct ScalarMapping{T,R,D} <: TensorMapping{T,R,D}
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87 λ::T
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88 range_size::NTuple{R,Int}
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89 domain_size::NTuple{D,Int}
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90 end
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91
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92 LazyTensors.apply(m::ScalarMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = m.λ*v[I...]
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93 LazyTensors.range_size(m::ScalarMapping) = m.domain_size
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94 LazyTensors.domain_size(m::ScalarMapping) = m.range_size
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95
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96 A = ScalarMapping{Float64,1,1}(2.0, (3,), (3,))
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97 B = ScalarMapping{Float64,1,1}(3.0, (3,), (3,))
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98
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99 v = [1.1,1.2,1.3]
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100 for i ∈ eachindex(v)
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101 @test ((A+B)*v)[i] == 2*v[i] + 3*v[i]
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102 end
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103
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104 for i ∈ eachindex(v)
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105 @test ((A-B)*v)[i] == 2*v[i] - 3*v[i]
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106 end
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107
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108 @test range_size(A+B) == range_size(A) == range_size(B)
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109 @test domain_size(A+B) == domain_size(A) == domain_size(B)
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110 end
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111
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112
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113 @testset "TensorMappingComposition" begin
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114 A = rand(2,3)
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115 B = rand(3,4)
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116
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117 Ã = LazyLinearMap(A, (1,), (2,))
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118 B̃ = LazyLinearMap(B, (1,), (2,))
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119
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120 @test Ã∘B̃ isa TensorMappingComposition
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121 @test range_size(Ã∘B̃) == (2,)
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122 @test domain_size(Ã∘B̃) == (4,)
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123 @test_throws SizeMismatch B̃∘Ã
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124
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125 # @test @inbounds B̃∘Ã # Should not error even though dimensions don't match. (Since ]test runs with forced boundschecking this is currently not testable 2020-10-16)
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126
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127 v = rand(4)
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128 @test Ã∘B̃*v ≈ A*B*v rtol=1e-14
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129
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130 v = rand(2)
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131 @test (Ã∘B̃)'*v ≈ B'*A'*v rtol=1e-14
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132 end
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133
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134 @testset "LazyLinearMap" begin
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135 # Test a standard matrix-vector product
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136 # mapping vectors of size 4 to vectors of size 3.
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137 A = rand(3,4)
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138 Ã = LazyLinearMap(A, (1,), (2,))
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139 v = rand(4)
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140 w = rand(3)
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141
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142 @test à isa LazyLinearMap{T,1,1} where T
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143 @test à isa TensorMapping{T,1,1} where T
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144 @test range_size(Ã) == (3,)
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145 @test domain_size(Ã) == (4,)
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146
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147 @test Ã*ones(4) ≈ A*ones(4) atol=5e-13
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148 @test Ã*v ≈ A*v atol=5e-13
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149 @test Ã'*w ≈ A'*w
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150
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151 A = rand(2,3,4)
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152 @test_throws DomainError LazyLinearMap(A, (3,1), (2,))
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153
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154 # Test more exotic mappings
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155 B = rand(3,4,2)
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156 # Map vectors of size 2 to matrices of size (3,4)
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157 B̃ = LazyLinearMap(B, (1,2), (3,))
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158 v = rand(2)
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159
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160 @test range_size(B̃) == (3,4)
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161 @test domain_size(B̃) == (2,)
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162 @test B̃ isa TensorMapping{T,2,1} where T
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163 @test B̃*ones(2) ≈ B[:,:,1] + B[:,:,2] atol=5e-13
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164 @test B̃*v ≈ B[:,:,1]*v[1] + B[:,:,2]*v[2] atol=5e-13
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165
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166 # Map matrices of size (3,2) to vectors of size 4
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167 B̃ = LazyLinearMap(B, (2,), (1,3))
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168 v = rand(3,2)
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169
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170 @test range_size(B̃) == (4,)
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171 @test domain_size(B̃) == (3,2)
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172 @test B̃ isa TensorMapping{T,1,2} where T
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173 @test B̃*ones(3,2) ≈ B[1,:,1] + B[2,:,1] + B[3,:,1] +
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174 B[1,:,2] + B[2,:,2] + B[3,:,2] atol=5e-13
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175 @test B̃*v ≈ B[1,:,1]*v[1,1] + B[2,:,1]*v[2,1] + B[3,:,1]*v[3,1] +
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176 B[1,:,2]v[1,2] + B[2,:,2]*v[2,2] + B[3,:,2]*v[3,2] atol=5e-13
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177
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178
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179 # TODO:
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180 # @inferred (B̃*v)[2]
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181 end
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182
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183
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184 @testset "IdentityMapping" begin
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185 @test IdentityMapping{Float64}((4,5)) isa IdentityMapping{T,2} where T
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186 @test IdentityMapping{Float64}((4,5)) isa TensorMapping{T,2,2} where T
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187 @test IdentityMapping{Float64}((4,5)) == IdentityMapping{Float64}(4,5)
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188
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189 @test IdentityMapping(3,2) isa IdentityMapping{Float64,2}
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190
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191 for sz ∈ [(4,5),(3,),(5,6,4)]
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192 I = IdentityMapping{Float64}(sz)
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193 v = rand(sz...)
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194 @test I*v == v
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195 @test I'*v == v
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196
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197 @test range_size(I) == sz
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198 @test domain_size(I) == sz
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199 end
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200
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201 I = IdentityMapping{Float64}((4,5))
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202 v = rand(4,5)
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203 @inferred (I*v)[3,2]
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204 @inferred (I'*v)[3,2]
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205 @inferred range_size(I)
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206
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207 @inferred range_dim(I)
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208 @inferred domain_dim(I)
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209
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210 Ã = rand(4,2)
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211 A = LazyLinearMap(Ã,(1,),(2,))
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212 I1 = IdentityMapping{Float64}(2)
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213 I2 = IdentityMapping{Float64}(4)
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214 @test A∘I1 == A
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215 @test I2∘A == A
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216 @test I1∘I1 == I1
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217 @test_throws SizeMismatch I1∘A
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218 @test_throws SizeMismatch A∘I2
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219 @test_throws SizeMismatch I1∘I2
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220 end
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221
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222 @testset "InflatedTensorMapping" begin
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223 I(sz...) = IdentityMapping(sz...)
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224
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225 Ã = rand(4,2)
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226 B̃ = rand(4,2,3)
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227 C̃ = rand(4,2,3)
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228
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229 A = LazyLinearMap(Ã,(1,),(2,))
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230 B = LazyLinearMap(B̃,(1,2),(3,))
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231 C = LazyLinearMap(C̃,(1,),(2,3))
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232
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233 @testset "Constructors" begin
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234 @test InflatedTensorMapping(I(3,2), A, I(4)) isa TensorMapping{Float64, 4, 4}
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235 @test InflatedTensorMapping(I(3,2), B, I(4)) isa TensorMapping{Float64, 5, 4}
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236 @test InflatedTensorMapping(I(3), C, I(2,3)) isa TensorMapping{Float64, 4, 5}
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237 @test InflatedTensorMapping(C, I(2,3)) isa TensorMapping{Float64, 3, 4}
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238 @test InflatedTensorMapping(I(3), C) isa TensorMapping{Float64, 2, 3}
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239 @test InflatedTensorMapping(I(3), I(2,3)) isa TensorMapping{Float64, 3, 3}
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240 end
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241
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242 @testset "Range and domain size" begin
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243 @test range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4)
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244 @test domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4)
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245
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246 @test range_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,4,2,4)
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247 @test domain_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,3,4)
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248
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249 @test range_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,4,2,3)
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250 @test domain_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,2,3,2,3)
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251
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252 @inferred range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4)
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253 @inferred domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4)
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254 end
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255
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256 @testset "Application" begin
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257 # Testing regular application and transposed application with inflation "before", "after" and "before and after".
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258 # The inflated tensor mappings are chosen to preserve, reduce and increase the dimension of the result compared to the input.
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259 tests = [
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260 (
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261 InflatedTensorMapping(I(3,2), A, I(4)),
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262 (v-> @tullio res[a,b,c,d] := Ã[c,i]*v[a,b,i,d]), # Expected result of apply
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263 (v-> @tullio res[a,b,c,d] := Ã[i,c]*v[a,b,i,d]), # Expected result of apply_transpose
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264 ),
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265 (
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266 InflatedTensorMapping(I(3,2), B, I(4)),
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267 (v-> @tullio res[a,b,c,d,e] := B̃[c,d,i]*v[a,b,i,e]),
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268 (v-> @tullio res[a,b,c,d] := B̃[i,j,c]*v[a,b,i,j,d]),
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269 ),
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270 (
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271 InflatedTensorMapping(I(3,2), C, I(4)),
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272 (v-> @tullio res[a,b,c,d] := C̃[c,i,j]*v[a,b,i,j,d]),
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273 (v-> @tullio res[a,b,c,d,e] := C̃[i,c,d]*v[a,b,i,e]),
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274 ),
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275 (
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276 InflatedTensorMapping(I(3,2), A),
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277 (v-> @tullio res[a,b,c] := Ã[c,i]*v[a,b,i]),
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278 (v-> @tullio res[a,b,c] := Ã[i,c]*v[a,b,i]),
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279 ),
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280 (
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281 InflatedTensorMapping(I(3,2), B),
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282 (v-> @tullio res[a,b,c,d] := B̃[c,d,i]*v[a,b,i]),
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283 (v-> @tullio res[a,b,c] := B̃[i,j,c]*v[a,b,i,j]),
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284 ),
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285 (
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286 InflatedTensorMapping(I(3,2), C),
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287 (v-> @tullio res[a,b,c] := C̃[c,i,j]*v[a,b,i,j]),
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288 (v-> @tullio res[a,b,c,d] := C̃[i,c,d]*v[a,b,i]),
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289 ),
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290 (
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291 InflatedTensorMapping(A,I(4)),
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292 (v-> @tullio res[a,b] := Ã[a,i]*v[i,b]),
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293 (v-> @tullio res[a,b] := Ã[i,a]*v[i,b]),
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294 ),
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295 (
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296 InflatedTensorMapping(B,I(4)),
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297 (v-> @tullio res[a,b,c] := B̃[a,b,i]*v[i,c]),
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298 (v-> @tullio res[a,b] := B̃[i,j,a]*v[i,j,b]),
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299 ),
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300 (
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301 InflatedTensorMapping(C,I(4)),
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302 (v-> @tullio res[a,b] := C̃[a,i,j]*v[i,j,b]),
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303 (v-> @tullio res[a,b,c] := C̃[i,a,b]*v[i,c]),
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304 ),
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305 ]
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306
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307 @testset "apply" begin
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308 for i ∈ 1:length(tests)
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309 tm = tests[i][1]
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310 v = rand(domain_size(tm)...)
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311 true_value = tests[i][2](v)
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312 @test tm*v ≈ true_value rtol=1e-14
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313 end
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314 end
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315
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316 @testset "apply_transpose" begin
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317 for i ∈ 1:length(tests)
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318 tm = tests[i][1]
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319 v = rand(range_size(tm)...)
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320 true_value = tests[i][3](v)
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321 @test tm'*v ≈ true_value rtol=1e-14
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322 end
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323 end
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324
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325 @testset "Inference of application" begin
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326 struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
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327 λ::T
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328 size::NTuple{D,Int}
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329 end
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330
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331 LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
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332 LazyTensors.range_size(m::ScalingOperator) = m.size
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333 LazyTensors.domain_size(m::ScalingOperator) = m.size
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334
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335 tm = InflatedTensorMapping(I(2,3),ScalingOperator(2.0, (3,2)),I(3,4))
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336 v = rand(domain_size(tm)...)
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337
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338 @inferred apply(tm,v,1,2,3,2,2,4)
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339 @inferred (tm*v)[1,2,3,2,2,4]
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340 end
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341 end
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342
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343 @testset "InflatedTensorMapping of InflatedTensorMapping" begin
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344 A = ScalingOperator(2.0,(2,3))
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345 itm = InflatedTensorMapping(I(3,2), A, I(4))
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346 @test InflatedTensorMapping(I(4), itm, I(2)) == InflatedTensorMapping(I(4,3,2), A, I(4,2))
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347 @test InflatedTensorMapping(itm, I(2)) == InflatedTensorMapping(I(3,2), A, I(4,2))
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348 @test InflatedTensorMapping(I(4), itm) == InflatedTensorMapping(I(4,3,2), A, I(4))
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349
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350 @test InflatedTensorMapping(I(2), I(2), I(2)) isa InflatedTensorMapping # The constructor should always return its type.
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351 end
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352 end
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353
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354 @testset "split_index" begin
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355 @test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4))
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356 @test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4))
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357 @test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,))
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358 @test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,))
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359 @test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3))
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360 @test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3))
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361
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362 @test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3))
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363 @test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6))
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364
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365 @inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4)
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366 end
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367
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368 @testset "slice_tuple" begin
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369 @test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3)
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370 @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5)
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371 @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3)
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372 @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6)
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373 end
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374
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375 @testset "split_tuple" begin
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376 @testset "2 parts" begin
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377 @test LazyTensors.split_tuple((),Val(0)) == ((),())
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378 @test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,))
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379 @test LazyTensors.split_tuple((1,),Val(1)) == ((1,),())
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380
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381 @test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4))
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382 @test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4))
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383 @test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4))
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384 @test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,))
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385 @test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),())
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386
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387 @test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,))
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388
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389 @inferred LazyTensors.split_tuple((1,2,3,4),Val(3))
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390 @inferred LazyTensors.split_tuple((1,2,true,4),Val(3))
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391 end
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392
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393 @testset "3 parts" begin
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394 @test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),())
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395 @test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,))
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396 @test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,))
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397
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398 @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6))
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399 @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,))
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400
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401 @inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2))
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402 @inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1))
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403 end
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404 end
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405
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406 @testset "flatten_tuple" begin
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407 @test LazyTensors.flatten_tuple((1,)) == (1,)
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408 @test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6)
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409 @test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6)
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410 @test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6)
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411 @test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6)
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412 end
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413
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414
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415 @testset "LazyOuterProduct" begin
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416 struct ScalingOperator{T,D} <: TensorMapping{T,D,D}
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417 λ::T
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418 size::NTuple{D,Int}
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419 end
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420
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421 LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...]
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422 LazyTensors.range_size(m::ScalingOperator) = m.size
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423 LazyTensors.domain_size(m::ScalingOperator) = m.size
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424
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425 A = ScalingOperator(2.0, (5,))
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426 B = ScalingOperator(3.0, (3,))
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427 C = ScalingOperator(5.0, (3,2))
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428
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429 AB = LazyOuterProduct(A,B)
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430 @test AB isa TensorMapping{T,2,2} where T
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431 @test range_size(AB) == (5,3)
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432 @test domain_size(AB) == (5,3)
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433
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434 v = rand(range_size(AB)...)
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435 @test AB*v == 6*v
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436
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437 ABC = LazyOuterProduct(A,B,C)
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438
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439 @test ABC isa TensorMapping{T,4,4} where T
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440 @test range_size(ABC) == (5,3,3,2)
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441 @test domain_size(ABC) == (5,3,3,2)
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442
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443 @test A⊗B == AB
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444 @test A⊗B⊗C == ABC
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445
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446 A = rand(3,2)
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447 B = rand(2,4,3)
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448
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449 v₁ = rand(2,4,3)
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450 v₂ = rand(4,3,2)
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451
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452 Ã = LazyLinearMap(A,(1,),(2,))
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453 B̃ = LazyLinearMap(B,(1,),(2,3))
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454
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455 ÃB̃ = LazyOuterProduct(Ã,B̃)
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456 @tullio ABv[i,k] := A[i,j]*B[k,l,m]*v₁[j,l,m]
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457 @test ÃB̃*v₁ ≈ ABv
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458
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459 B̃Ã = LazyOuterProduct(B̃,Ã)
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460 @tullio BAv[k,i] := A[i,j]*B[k,l,m]*v₂[l,m,j]
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461 @test B̃Ã*v₂ ≈ BAv
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462
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463 @testset "Indentity mapping arguments" begin
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464 @test LazyOuterProduct(IdentityMapping(3,2), IdentityMapping(1,2)) == IdentityMapping(3,2,1,2)
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465
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466 Ã = LazyLinearMap(A,(1,),(2,))
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467 @test LazyOuterProduct(IdentityMapping(3,2), Ã) == InflatedTensorMapping(IdentityMapping(3,2),Ã)
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468 @test LazyOuterProduct(Ã, IdentityMapping(3,2)) == InflatedTensorMapping(Ã,IdentityMapping(3,2))
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469
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470 I1 = IdentityMapping(3,2)
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471 I2 = IdentityMapping(4)
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472 @test I1⊗Ã⊗I2 == InflatedTensorMapping(I1, Ã, I2)
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473 end
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474
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475 end